Manager + Worker Agents — Planning That Scales

Highlights: A strong planner on top of a small worker lifts OSWorld success from 25% to 58%. Daniel Fried’s lecture in CMU 11-768 AI Agents (Fall 2026) analyzes modular system designs that decouple high-level planning from low-level grounded execution. We walk through the trade-offs between prolonged internal deliberation and empirical interaction scaling, the compounding failure modes that destabilize long-horizon trajectories, and multi-agent coordination frameworks for desktop computer use.

• Cross-model specialization: Pairing a frontier strategic planner with a lightweight 4B or 7B grounded executor reduces inference costs while boosting OSWorld completion rates to 58.3%.
• Interaction scaling: Spending inference compute on iterative environment actions and sensory feedback consistently outperforms prolonged chain-of-thought deliberation.
• Overthinking pathologies: Unchecked internal reasoning on coding benchmarks triggers analysis paralysis, rogue environment modifications, and premature task termination.
• Compounding drift: Step-level errors compound exponentially over long horizons, while autoregressive self-conditioning causes models to treat past context mistakes as patterns to follow.
• Plans as security boundaries: Upfront plan-then-execute pipelines constrain untrusted web inputs to typed parameters, neutralizing adversarial prompt injections.

Tutorial Overview:

1. Modularity: Specializing Planners and Executors
2. Synthetic Trajectory Training and Role Decomposition Pitfalls
3. Grounding and Affordances
4. Dynamic Replanning on Feedback Loops
5. Interaction Scaling vs. Prolonged Chain-of-Thought
6. Overthinking Pathologies and Calibration Before Action
7. Execution Horizon Fragility and Compounding Drift
8. Model Scale, Self-Conditioning, and Subagent Delegation
9. Plans as Security Boundaries Against Prompt Injection
10. Human Oversight and Staged Plan Execution
11. Multi-Agent Computer Use Architecture
12. MACU Ablations on OSWorld and Odysseys
13. Summary

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1. Modularity: Specializing Planners and Executors

In short
– Modular agents decouple high-level strategic planners from low-level grounded executors to lower costs.
– Small 7B models handle screen coordinates while frontier models manage semantic subgoals.

1.1 Decoupling planners from grounded executors

Decomposing agent systems separates high-level strategy from fine-grained motor control.
– Targeted hill-climbing: teams iterate on individual sub-systems without retraining the complete pipeline.
– Inference efficiency: modularity lowers operational costs even when frontier unified models match performance.
– Workflow induction: modular designs mirror segmenting recorded web interactions into structured sub-tasks.
– 93 discrete actions: Odysseys surgeon searches require extensive typing and clicks across websites.

Unified models waste compute generating low-level actions. Decoupled modules allow independent optimization of planning and execution.

Fig 1. One model doing both reasoning and grounding versus a planner and executor that can be improved separately.

1.2 Cross-model specialization for coordinate execution

Large vision-language planners excel at strategy but struggle with pixel-level precision.
– Cross-model specialization [pairing a large semantic model for strategic planning with a lightweight model dedicated to fine-grained, low-level execution]: GPT-4o directs plans while specialized grounders handle pixels.
– 7B parameter models: UGround and Agent S offload screen coordinates to compact local networks.
– Regression shielding: delegating coordinate prediction prevents spatial regression overhead in the planner.
– Structural brittleness: omitting rich environmental context during sub-task handoffs causes compounding failures.

Frontier planners lack fine spatial resolution for interface elements. Compact grounders achieve high coordinate accuracy at low cost.

Fig 2. Lightweight grounding models translate strategic subgoals into exact pixel coordinates.

1.3 Synthesizing supervision with Plan-and-Act

Naturally occurring web trajectories lack explicit high-level plans paired with actions.
1. Filter collected web trajectories down to verified successful demonstrations.
2. Prompt a teacher model to segment raw action sequences into coherent sub-tasks.
3. Generate natural language labels describing each segmented phase.
4. Fine-tune both the planner for subgoals and the executor for grounded actions.

Fried highlights that natural demonstrations lack subgoal labels. Synthetic segmentation generates paired training data without manual annotation.

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2. Synthetic Trajectory Training and Role Decomposition Pitfalls

In short
– Synthetic supervision pipelines isolate agent components by generating paired plan-action training data.
– Teacher LLMs segment successful execution traces to train specialized planners and executors.
– Rigid multi-agent role structures fail when condensed handoffs discard critical execution context.

2.1 Supervised Planner Fine-Tuning

Synthetic supervision bridges the gap between high-level reasoning and unannotated environment actions.
– Modular designs: developers decouple planning and execution modules to optimize each component independently.
– Supervision gap: natural environments lack explicit pairings of high-level subgoals with low-level execution traces.
– Bottleneck isolation: modular pipelines isolate operational failure points without retraining entire foundation models.

Fig 3. Real environments give no paired plans and actions, so training data for the planner is synthesized.

Raw execution logs lack explicit subgoals. Synthetic pipelines generate paired annotations to supervise specialized modules.

2.2 Plan-and-Act Trajectory Labeling

The Plan-and-Act framework (Erdogan et al., ICML 2025) extracts training corpora directly from unannotated trajectories.
1. Teacher LLM segments raw action sequences into coherent subtask boundaries.
2. Teacher LLM annotates each segmented sequence with natural language descriptions.
– Planner training: the model learns to output structured sequences of abstract subgoals.
– Executor training: the model learns to ground individual subgoals into concrete environment actions.
– Paired corpora: unzipping labeled trajectories trains planners and executors simultaneously.

Specialized training requires isolated data streams. Segmenting traces enables targeted optimization for distinct roles.

2.3 Hierarchical Decomposition Trade-Offs

Dividing complex tasks into subtasks creates engineering trade-offs between context length and execution nuance.

Architecture	Context load	Action history	Failure risk
Unified single agent	High context growth	Retains full action history	Context exhaustion
Modular pipeline	Constrained per subtask	Discards inter-module history	Boundary context loss

• Subtask planning: abstract plans shorten context windows for individual low-level actions.
• Abstraction penalty: high-level subgoals often overlook subtle low-level environment nuances.
• Boundary risk: modular pipelines fail catastrophically when critical context drops between stages.

Subtask abstraction minimizes prompt size. However, losing low-level nuances can trigger irrecoverable execution errors.

2.4 Rigid Role Decomposition Pitfalls

Rigid multi-agent software engineering setups face severe practical limitations (Neubig, 2024; CodeR, Chen et al., 2024).
– Role splitting: pipelines partition tasks across reproducer, fault localizer, editor, and verifier roles.
– Adaptability limits: verifiers cannot localize faults independently to inspect their own proposed solutions.
– Lossy handoffs: inter-agent communication relies on condensed summary reports that drop essential context.
– Single-agent retention: unified architectures avoid communication bottlenecks by retaining complete execution histories directly.

Static role handoffs discard granular details during unexpected errors. Single agents adapt faster because they preserve full operational history.

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3. Grounding and Affordances

In short
– Decouple strategic planning from execution to prevent low-level errors from corrupting task logic.
– Affordance scoring filters impossible actions by multiplying semantic priors by physical feasibility.
– Closed-loop replanning with environment feedback resolves ambiguities faster than isolated reasoning.

3.1 Curating trajectories for dynamic control

Unannotated execution traces supply training data for modular planners and executors through trajectory unzipping workflows.

Architecture	Context handling	Schema flexibility	Best task fit
Monolithic baseline	Retains full history	High	Dynamic open environments
Structured (Agentless)	Lossy module handoffs	Low	Fixed schemas (GitHub issues)

• Plan-and-Act framework: derives training data directly from unannotated execution traces via paired unzipping.
• Workflow induction: mirrors demonstrated traces but risks discarding subtle low-level execution nuances.
• Role decomposition: assigns distinct agents to testing, editing, and product management roles.
• 93-step Odysseys task: running long-horizon surgeon searches across websites fails if role handoffs drop context.

Fig 4. Segmenting execution trajectories creates specialized training pairs for planners and executors.

Rigid handoffs drop critical interaction context during transitions. Trajectory unzipping preserves execution nuances across subtasks.

3.2 Training planners for sequential scheduling

Decoupled pipelines train semantic planners on strategic milestones while grounding models handle low-level tool execution.

• Curated milestone pairs: train planners on abstract subgoals rather than raw API arguments.
• Cross-model specialization [pairing a strategic planner with a low-level executor]: isolates high-level scheduling logic from low-level execution errors.
• Downstream grounding: translates isolated subgoals into valid, error-free environment interactions.
• Error isolation: prevents low-level execution mistakes from compounding into strategic planning logic.

Fig 5. Curating milestone pairs: successful runs are segmented into milestones that train the planner.

Planners handle strategic intent without tracking low-level parameters. Grounding models resolve concrete tool arguments independently.

3.3 SayCan scoring and physical affordances

SayCan combines language model probabilities with robotic value functions to ensure actions are physically executable.

• SayCan (Ahn et al., CoRL 2022): models physical affordances [action success probability given environment state] using value functions.
• Precondition checks: require holding an object before assigning high affordance value to placing it.
• 0.0 affordance score: near-zero values suppress actions targeting absent objects like missing apples.
• Exploratory behaviors: receive high affordance values when target objects remain unobserved.
• Joint selection formula: evaluates $P(\text{action} \mid \text{state}, \text{instruction}) \propto P_{\text{LLM}}(\text{action} \mid \text{instruction}) \times P_{\text{affordance}}(\text{action} \mid \text{state})$.
• Textual replanning: LLM-Planner (Song et al., ICCV 2023) and Inner Monologue (Huang et al., CoRL 2022) revise ALFRED potato-cooking schedules after finding closed refrigerators.
• Interaction scaling [allocating test-time compute to environment feedback]: Reflexion and SwiftSage (Lin et al., NeurIPS 2023) beat prolonged reasoning traces.

Language models propose linguistically plausible actions that fail physically. Multiplying semantic priors by affordances prevents executing unfeasible tasks.

3.4 Dynamic closed-loop action rescoring

Continual value function evaluation rescores action probabilities as physical environment states change during execution.

1. Observe state: capture sensory observations and detect physical changes in the environment.
2. Evaluate affordances: calculate physical feasibility probabilities across candidate actions using learned value functions.
3. Rescore actions: multiply semantic priors by affordances to select the optimal executable step.
4. Execute and recover: dispatch valid actions to eliminate infinite retry loops on blocked paths.

Fig 6. Closed-loop rescoring dynamically re-evaluates candidate actions against immediate sensory observations.

Static plans fail when environment conditions change unexpectedly. Continuous rescoring enables agents to recover from physical disturbances.

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4. Dynamic Replanning on Feedback Loops

In short
– Static plans break when environment preconditions fail unexpectedly during execution.
– Closed-loop inner monologues update subgoals using runtime environmental observations.
– Interaction scaling outperforms extended internal reasoning under fixed inference budgets.

4.1 Closed-loop plan updating

Agents must adjust unfinished plans when runtime preconditions fail in partially observed environments.
– ALFRED task: agent cooking a potato cannot proceed if target food is not visible.
– Precondition failure: static plans stall without direct line-of-sight to target items.
– Inner monologue: executor feeds natural language observations back into the planning prompt.
– Adaptive replanning: LLM inspects a visible refrigerator first to find the potato.

Observations let models recover dynamically because runtime feedback grounds subsequent choices.

4.2 Balancing thinking and acting

Agents divide inference compute budgets between internal deliberation and empirical environment interaction.
– Extended reasoning: models generate longer chain-of-thought traces before issuing actions.
– Trajectory sampling: external judges score multiple sampled candidate paths.
– Interaction scaling [Allocating test-time compute to taking actions and receiving empirical environment feedback rather than generating prolonged internal reasoning traces.]: agents gather real-time state observations across environment steps.
– Performance edge: active environment interaction beats prolonged reasoning for fixed compute budgets.

Fig 7. Environment interaction yields higher task returns per compute token than internal deliberation.

Real-world feedback corrects inaccurate assumptions before errors compound. Irreversible actions remain the primary operational risk.

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5. Interaction Scaling vs. Prolonged Chain-of-Thought

In short
– Action tokens with environment feedback outperform ungrounded internal deliberation under matched compute budgets.
– Excessive reasoning causes analysis paralysis, rogue action chains, and premature task disengagement.
– High single-step precision and confidence calibration prevent catastrophic compounding errors across long horizons.

5.1 Inference Regimes for Agent Scaling

Developers choose between internal model deliberation and direct environmental interaction to spend test-time compute.

Regime	Mechanism	Primary Cost	Feedback Source
Budget forcing	Generates longer reasoning chains	Compute tokens	Internal self-reflection
Best-of-N	Samples candidate trajectories	Parallel sampling	External verifier
Interaction scaling	Executes iterative actions	Environment actions	Empirical observations

• Empirical superiority: interaction scaling (spending compute on actions and sensory observations rather than prolonged reasoning) beats deliberation.
• Odysseys example: executing 93 discrete actions across websites grounds decisions better than pure upfront planning.

Sensory feedback provides reality checks that internal reasoning cannot invent. Grounded execution prevents models from hallucinating state changes.

5.2 The Perils of Overthinking

Excessive deliberation degrades coding performance across interactive benchmarks like SWE-bench.

• Analysis paralysis: compute tokens exhaust the budget on endless planning rather than issuing actions.
• Rogue action chains: executing unhelpful state-altering commands corrupts and pollutes the target environment.
• Premature disengagement: the model terminates execution early before completing the assigned task.
• Negative correlation: higher overthinking scores directly predict lower task success across model families.
• OpenAI o1: higher reasoning effort calibrates deliberation better than uncalibrated smaller reasoning systems.

Unchecked reasoning drifts into circular deliberation without environmental grounding. Stronger models mitigate this by balancing deliberation against action execution.

5.3 Calibrate Before Taking Irreversible Action

Calibrate-then-Act balances internal inference with environmental inspection when facing hidden file formats.

• 12 format combinations: delimiter, quote character, and skipped header rows create hidden state ambiguity.
• Confidence calibration: explicitly estimating prior probabilities prompts models to run unit tests before executing.
• 0.85 delimiter likelihood: remaining uncertainty triggers verification actions rather than blind guess-and-go data transformations.

Additional context:
 • Estimated format likelihoods
   are provided below.

Format likelihoods:
{prior}

Fig 8. Calibrating format probabilities prompts inspection before applying irreversible file transformations.

Supplying explicit uncertainty prompts models to inspect the environment first. Verifying state variables prevents irreversible data corruption.

5.4 Compounding Errors in Long Horizons

Long-horizon tasks fail from cascading step-level mistakes rather than deficits in deep abstract reasoning.

• 222 simulated days: Vending-Bench demands up to 2,000 interactions and approximately 25 million tokens.
• 0.90 vs. 0.999 precision: step accuracy decays exponentially at $0.90^T$, whereas 0.999 sustains long survival horizons.
• Self-conditioning: autoregressive models treat context errors as predictive patterns, generating further mistakes.
• Qwen3-32B vulnerability: large models suffer severe context poisoning from uncorrected historical execution errors.
• Reinforcement learning: RL trains agents to recover from mistakes independently of contaminated historical context.

Sequential failure cascades through three specific stages:

1. The agent attempts premature restocking by confusing due today with already delivered.
2. The agent interprets unavailable now as business failure, ignoring subsequent delivery notices.
3. The agent reports routine daily operating fees as fraudulent termination charges.

Compounding drift turns small early misinterpretations into irreversible trajectory collapse. Reinforcement learning provides recovery policies when context histories become toxic.

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6. Overthinking Pathologies and Calibration Before Action

In short
– Excessive ungrounded reasoning causes analysis paralysis, rogue action chains, and premature task abandonment.
– High overthinking scores negatively correlate with issue resolution success on SWE-bench.
– Calibrate-then-Act injects prior state probabilities into prompts, forcing agents to test before executing.

6.1 Overthinking Pathologies in Coding Benchmarks

Ungrounded deliberation without environment feedback triggers severe behavioral breakdowns during automated software engineering tasks.

• Analysis paralysis: agent exhausts its compute budget planning and deliberating rather than issuing actions.
• Rogue action chains: agent executes unhelpful, state-altering commands that corrupt the environment repository.
• Premature disengagement: model incorrectly terminates execution before completing the assigned task.
• Source: Cuadron et al. (2025), co-authored by Graham Neubig; highlighted by Daniel Fried in the lecture.

Fig 9. The three overthinking failure modes from Cuadron et al. (2025).

Models replace concrete tool interactions with speculative loops. These runaway internal deliberations corrupt states or quit prematurely.

6.2 Quantifying Overthinking on SWE-bench

Classifiers track overthinking by comparing internal token expenditure directly against external tool interactions.

• Overthinking score: ratio of commercial API reasoning tokens to externally observable actions executed.
• SWE-bench correlation: strong negative correlation connects high overthinking scores to lower issue resolution.
• OpenAI o1 dynamics: higher reasoning effort reduces overthinking, whereas lower reasoning effort yields worse outcomes.
• Non-reasoning LLMs: scratchpad text creates overthinking tendencies, but degradation is less severe than reasoning models.

Speculative internal reasoning yields diminishing returns. Agents solve tasks more reliably through empirical environment feedback.

6.3 Calibrate-then-Act Suppresses Guessing Errors

Agents frequently assume environment structures without inspection, such as querying a price column without checking schemas.

• Calibrate-then-Act (CTA): framework by Ding et al. (2026) and Sun et al. (COLM 2026).
• Confidence calibration: explicitly supplies prior probabilities over unobserved environment states to prompt verification.
• 12 format combinations: resolves 3 delimiters (,, ;, \t), 2 quotes (", '), and 2 header offsets (0, 1).
• ~0.85 format likelihood: estimated probability for ; prompts unit tests before blind execution.

Additional context:
 • Estimated format likelihoods
   are provided below.

Format likelihoods:
{prior}

Fig 10. Calibrate-then-Act supplies format likelihoods to trigger unit testing before action execution.

Explicit prior probabilities reveal residual uncertainty to the model. Agents then verify formats using tests rather than ungrounded guesses.

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7. Execution Horizon Fragility and Compounding Drift

In short
– Compounding drift collapses multi-step success unless per-step reliability approaches near-perfect accuracy.
– Autoregressive self-conditioning amplifies historical mistakes unless policies learn recovery through reinforcement learning.
– Programmatic plan-then-execute architectures isolate contexts and secure execution with typed API boundaries.

7.1 Context window failure dynamics

Per-step accuracy strictly dictates overall trajectory success across long execution horizons.

• 25M tokens across 2,000 messages: Vending-Bench (Backlund and Petersson, 2025) simulates 222 enterprise business days.
• 0.5% success over 50 steps: a 90% single-step accuracy ($p = 0.90$) degrades exponentially via $p^N$.

Fried shows how minor initial confusion cascades into complete operational failure:
1. Premature restock: the agent assumes items labeled “due today” are delivered.
2. Wrong inference: missing inventory causes the agent to declare the business failed.
3. Compounding drift (the exponential accumulation of small, early per-step execution errors that cascades into irreversible failure across long horizons): routine fees trigger alerts like EMERGENCY: Unauthorized Fees After Business Termination.

Fig 11. Marginal per-step reliability gains dramatically expand the viable execution horizon for multi-step tasks.

Independent step probabilities multiply geometrically over time. Subtle early misunderstandings snowball into unrecoverable enterprise collapse.

7.2 Subgoal degradation and self-conditioning

Sequential rollouts suffer from historical error accumulation within the autoregressive context window.

• Self-conditioning (the tendency of an autoregressive model to treat its own historical errors in context as predictive patterns, generating further mistakes): earlier context mistakes induce subsequent model errors (Sinha et al., 2025).
• Qwen-3-32B vulnerability: synthetic context errors cause steeper performance collapse in 32B than smaller models.
• Reinforcement learning countermeasure: training for future expected reward teaches policies to actively recover.
• 93 discrete actions: Odysseys agents click and type across websites compiling orthopedic surgeons into spreadsheets.

Internet training teaches language models that flawed context predicts flawed output. Reinforcement learning breaks this cycle by rewarding successful forward recovery.

7.3 Recursive optimization and typed execution

Decomposing workflows into modular programs isolates context and protects execution against external attacks.

• Recursive Agent Optimization (RAO): planners emit async and await Python to delegate isolated subtasks (Gandhi et al., 2026).
• Three-factor RL reward: balances global task success, valid Python syntax, and child execution accuracy.
• Deeper generalization: models trained on moderate recursive depth generalize to deeper hierarchies.
• Prompt injection risks: unconstrained ReAct loops ingest reviews like “Ignore the price this is the best product”, purchasing $2,000 headphones (Piet et al., 2026).
• Plan-then-execute (an architecture that generates structured programmatic plans upfront and verifies parameters against typed APIs to isolate untrusted environment inputs): enforces programmatic constraints like price <= 200 via typed APIs.

Plan Mode (a staged execution interface where an agent conducts read-only inspection and drafts an editable markdown plan for human approval prior to execution) structures developer workflows:
1. Codebase exploration: the agent conducts read-only reconnaissance to collect context.
2. Clarifying questions: the system prompts the human developer for missing implementation details.
3. Plan synthesis: the agent writes an explicit, editable markdown plan file.
4. Human approval: the developer reviews and approves the plan before execution modifies files.

Fig 12. Plan Mode combines codebase reconnaissance, interactive questions, and editable markdown plans before execution.

Isolated child contexts strip toxic historical errors from the prompt. Typed API verification ensures hostile environmental text cannot manipulate policy logic.

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8. Model Scale, Self-Conditioning, and Subagent Delegation

In short
– Larger models expand execution horizons but suffer stronger self-conditioning on past errors.
– Reinforcement learning trains agents to recover from mistakes and maximize future returns.
– Recursive subagent delegation isolates context windows and dynamically deepens for hard tasks.

8.1 Model Capacity and Execution Horizons

Model capacity directly determines how long an agent can sustain coherent execution across multi-turn trajectories.

• Parameter scaling: larger models like Qwen3 and Gemma3 sustain significantly longer execution horizons.
• Single-step accuracy: Fried cites Sinha et al. (2025) showing marginal accuracy gains unlock huge horizon increases.
• Compounding errors: extended trajectories degrade rapidly unless per-step accuracy remains exceptionally high.
• 93 actions: an Odysseys surgeon search requires 93 discrete web actions without compounding early mistakes.

Minor per-step execution errors compound exponentially, causing long-horizon multi-step workflows to fail prematurely.

8.2 Scale-Dependent Error Self-Conditioning

Larger models exhibit heightened vulnerability to self-conditioning (treating past errors in context as predictive patterns for further mistakes).

• Error conditioning: larger models condition strongly on early failures, derailing subsequent actions.
• 100 turns: Qwen3-32B experiences a dramatically steeper accuracy drop than Qwen3-4B after injected errors.
• Pre-training bias: web text and code associate low-quality tokens with further low-quality tokens.

Fig 13. Larger models extend execution horizons but suffer steeper accuracy drops from historical error conditioning.

Web pre-training teaches models that buggy syntax statistically predicts additional broken code down the line.

8.3 Mitigating Self-Conditioning via Training

Specialized training objectives and context boundaries insulate agent decisions from corrupted history.

• Reinforcement learning (RL): RL trains agents to recover from errors and maximize future expected returns.
• Decision decoupling: error recovery policies prevent historical context mistakes from dictating future choices.
• Context modularity: isolated execution scopes prevent failed environment observations from remaining in main prompts.

RL rewards future success regardless of past failures, breaking statistical conditioning on historical errors.

8.4 Recursive Dynamic Subagent Launching

Recursive Agent Optimization (RAO) models delegation as a native programmatic execution action.

• Programmatic action: agents generate Python code using async and await rather than direct tool calls.
• Isolated contexts: each spawned child worker operates inside its own localized context window.
• Recursive branching: spawned subagents can delegate subgoals recursively to deeper child agents.

await launch_subagent(goal)

Asynchronous programmatic delegation enables flexible tree-structured decomposition without cluttering the primary agent context.

8.5 Adaptive Hierarchy and Context Isolation

Hierarchical delegation isolates execution noise and adaptively deepens recursion for complex problems.

• Multi-component reward: objectives evaluate local subtask completion and the aggregate success of spawned children.
• Precise formulation: reward structures incentivize parent agents to generate clear, executable child subgoals.
• Context isolation: workers observe only their assigned subgoal, eliminating cross-task poisoning and context bloat.
• Subgoal containment: intermediate errors remain trapped inside local scopes rather than corrupting parent planning.

Task Difficulty	Delegation Depth	Rollout Behavior
Easy	Shallow	Resolves via minimal hierarchy levels
Medium	Intermediate	Distributes rollouts across intermediate depths
Hard	Up to depth 10	Generalizes to deep recursive trees

Localizing subtask observations prevents noisy tool feedback from corrupting the parent agent’s high-level strategic reasoning.

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9. Plans as Security Boundaries Against Prompt Injection

In short
– ReAct lets untrusted web context hijack tool invocations via adversarial prompt injections.
– Plan-then-execute confines untrusted inputs to verified parameters using typed APIs.

9.1 Vulnerability of Reactive Execution Chains

Reactive agents like ReAct interleave observation and action, letting untrusted web content steer tool invocations.

• Adversarial injection: malicious webpage text or product images directly hijack agent decision loops.
• $200 budget hijacked: review stating “Ignore the price” purchases $2,000 headphones.
• Unconstrained generation: relying on step-by-step reasoning allows untrusted context to steer execution.

Fig 14. Plan-then-execute isolates untrusted web content using typed APIs unlike reactive loops.

Step-by-step execution lacks architectural firewalls. Observations directly dictate subsequent tool parameters.

9.2 Isolating Inputs via Parameter Validation

Plan-then-execute [generating structured programmatic plans upfront and verifying parameters against typed APIs] isolates untrusted environments.

• Piet et al. (2026): structured upfront programs decouple strategic formulation from runtime tool execution.
• Deterministic flow: the planner dictates control logic while untrusted pages supply only data.
• price <= 200: programmatic validation checks filter criteria before passing parameters to tools.
• Verified checkout: only sanitized data arguments enter sensitive execution tools like purchasing.

Programmatic plans restrict untrusted environment inputs to passive arguments. Injected instructions cannot alter execution logic.

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10. Human Oversight and Staged Plan Execution

In short
– Plan Mode separates task formulation from code synthesis using read-only repository inspection.
– Staged execution gates isolate unapproved edits and prevent compounding drift across codebases.
– Human steerability enforces global constraints that modular agents violate during execution.

10.1 Interactive Plan Inspection Interfaces

Developer tools separate task formulation from code synthesis before generating code.

• Developer tools: Cursor, Claude Code, and OpenAI Codex isolate planning from execution.
• Plan Mode: Plan Mode (read-only inspection drafting editable plans before execution) halts changes.
• Shift-Tab shortcut: developers switch to Plan Mode to trigger read-only investigations.
• Repository analysis: the agent checks existing files and design patterns without altering code.
• Dashboard demo: Fried demonstrates building a background agent dashboard on cursor.com.

Early inspection grounds models in repository conventions. Unchecked edits break unfamiliar architectures.

10.2 Approval Gating and Subgoal Modification

Agents query developers about design decisions instead of guessing architectural intent.

• Targeted questions: the agent asks about routing conventions and database storage strategies.
• User interface choices: developers specify Kanban status groupings and comment card formatting.
• Plan artifact: the agent synthesizes user preferences into a structured plan.md file.
• Document structure: the plan outlines front-end data models, page routes, and tasks.
• Subgoal refinement: developers inspect and adjust proposed subgoals before granting execution authorization.

Clarifying queries resolve ambiguous requirements early. Programmers avoid rewriting completed implementations.

10.3 Staged Execution Controls and Checkpoints

Staged workflows lock file edits until developers approve the persistent plan artifact.

1. Inspect codebase: analyze repository patterns in read-only mode without modifying files.
2. Draft plan: synthesize user preferences into a structured plan.md document.
3. Approval gate: review and approve the persistent plan before unlocking file edits.
4. Trigger build: load approved markdown directly into the active context window.
5. Execute subgoals: convert planned tasks into active work items with checkpoints.

• Silent drift: staged execution stops compounding drift (exponential accumulation of small execution errors).
• Locked edits: file modifications remain completely locked until human review succeeds.
• Context loading: execution triggers only when users explicitly load plan.md.

Fig 15. Staged pipelines prevent compounding drift by inserting mandatory review checkpoints between planning and execution.

Checkpoints allow developers to halt execution when implementations diverge. Phased pipelines contain errors before files change.

10.4 Deterministic Governance in Agent Planning

Human supervisors intervene when model reasoning diverges from system requirements.

• Hypothesis nature: plans represent explicit hypotheses of future actions rather than guarantees.
• Implicit representations: models formulate plausible subgoals that diverge from developer architecture intent.
• Intermediate representations: supervisors inspect intermediate subgoals to identify flawed reasoning assumptions early.
• Defect prevention: deterministic governance stops early planning mistakes from corrupting entire projects.

Models lack ground-truth world representations. Human boundaries keep execution aligned with system specifications.

10.5 User-Supervised Trajectory Steerability in Environments

Complex computer use tasks require external steering to prevent navigation failures.

• Odysseys benchmark: Jang et al. (2026) compile realistic workflows from opted-in user histories.
• 93 actions: an agent searching for orthopedic surgeons executes 93 click and type actions.
• Multi-site navigation: the agent visits multiple websites to compile orthopedic surgeon information.
• Spreadsheet output: discovered surgeon findings are compiled into a structured spreadsheet.
• Cascade prevention: supervisory steering preserves task alignment across extended web sessions.

Fig 16. External steering guides long-horizon agents through dense web interfaces to prevent cascading errors.

Long-horizon browsing magnifies single errors. Without steering, early navigation mistakes invalidate entire interaction histories.

10.6 Cursor Plan Mode Checklist Interfaces

Cursor’s interface operationalizes plan authorization by rendering plans as living interactive checklists.

• Dynamic transitions: checklist items transition dynamically from pending to in-progress and completed states.
• Versioned markdown: storing plans as plan.md in workspaces enables asynchronous team collaboration.
• Context separation: persisting plans outside prompt context preserves high-level goals for worker agents.
• Inspection view: developers review frontend data models and route specifications before execution.

Fig 17. Versioned markdown plans provide shared state across human collaborators and autonomous worker agents.

Storing plans as files separates durable task state from transient context. Teams audit progress continuously without losing context.

10.7 Enforcing Global Plan Constraints

Modular agents solve local subtasks effectively but consistently violate global system constraints.

• TravelPlanner benchmark: Xie et al. (ICML 2024) evaluate trip planning over flights, hotels, attractions.
• Modular decomposition: agents decompose complex itineraries into successful isolated subtasks.
• Budget violations: agents consistently breach global constraints like total budgets and travel times.
• Dietary constraints: modular planning fails to satisfy cross-subtask constraints like shared dietary requirements.
• Human validation: human oversight acts as a global validator across isolated worker modules.

Local optimizers miss cross-cutting constraints. Human oversight validates aggregate plans against shared budgets.

10.8 Review Gates for Destructive Actions

Real-world actions carry asymmetric costs that require strict authorization boundaries before execution.

Action Type	Environmental Impact	Reversibility	Authorization Gate
Reasoning tokens	Internal scratchpad	Fully reversible	Fully autonomous
Web inspection	Read-only observation	Fully reversible	Fully autonomous
Tool mutations	Modifies files or state	Irreversible	Human approval

• Asymmetric costs: chain-of-thought tokens discard cheaply, but database modifications commit irreversibly.
• Exploratory authority: review gates restrict autonomous authority strictly to read-only information gathering.
• Milestone halts: agents must halt before executing financial transactions or procurement requisitions.

Discarding faulty thoughts costs pennies. Reversing unauthorized database commits or financial transactions costs fortunes.

10.9 Human-Assisted Subgoal Revision Cycles

Runtime discoveries invalidate initial assumptions and require mid-course corrections without restarting.

• Runtime discoveries: agents uncover unexpected dependencies, missing endpoints, or broken tests during runs.
• Premature disengagement: unguided systems risk premature disengagement (terminating before completing tasks) or erratic edits.
• In-place pruning: developers prune obsolete subgoals directly inside the active markdown checklist interface.
• Zero re-execution: mid-course revision updates remaining tasks without re-executing completed subtasks.

Static plans break against dynamic runtime environments. Live checklist editing preserves valid completed work.

10.10 Plan Modification and Boundary Enforcement

Natural language markdown documents establish transparent execution contracts between humans and models.

• Equalized communication: markdown interfaces allow humans to edit headers and rewrite task descriptions.
• Standard tooling: developers adjust model instructions using standard workspace text editing tools.
• Scope restriction: plans restrict file access and tool rights to explicit approved subdirectories.
• Unauthorized prevention: clear execution boundaries stop unauthorized code edits across sensitive repositories.

Shared plain-text contracts eliminate translation barriers. Explicit path boundaries contain rogue modifications.

10.11 Maintaining Oversight Across Agent Lifecycles

Reliable system design balances safety constraints against autonomous execution efficiency across lifecycles.

• Separability check: verify whether problems decompose into clean modular subtasks for specialized models.
• Environmental triggers: define which unexpected observations or tool execution failures require dynamic replanning.
• Context management: prevent accumulated historical errors from poisoning future model generation decisions.
• Authority separation: designate purely read-only operations and assign approval rights for irreversible actions.

Unbounded autonomy creates catastrophic operational risks. Rigid gating creates unacceptable development bottlenecks.

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11. Multi-Agent Computer Use Architecture

In short
– The MACU architecture separates high-level planning from low-level computer execution.
– Parallel subagents complete isolated web browsing tasks inside independent virtual machines.
– Dynamic replanning with cross-model specialization raises OSWorld success to 58.3%.

11.1 Manager DAG Generation

A frontier manager coordinates worker environments by generating a structured execution graph.

1. Subtask DAG (A dependency-aware graph of subtasks used by a manager agent to coordinate and dispatch parallel worker agents without circular loops): manager generates subtasks with dependencies from user requests.
2. Parallel execution: computer-using agent (CUA) workers run subtasks on dedicated virtual machines.
3. Synthesis loop: manager inspects outputs and triggers replanning or final deliverables.

Decoupling planning from tool execution isolates context windows. This prevents low-level action noise from corrupting task plans.

Fig 18. A manager DAG orchestrates parallel computer-using agents operating inside isolated virtual machines.

11.2 Long-Horizon Web Benchmarking

Real-world computer use benchmarks evaluate sustained execution over dozens of web interactions.

• Odysseys benchmark: grounds complex workflows in donated human browsing histories like surgeon searches.
• 93 discrete actions (running example): In an Odysseys evaluation task, an agent searching for specialized orthopedic surgeons must execute 93 discrete low-level click and type actions across multiple websites and compile findings into a spreadsheet.
• 30 to 60 minutes: duration required to navigate multi-tab workflows across real-world websites.
• Rubric compliance: evaluations check strict completion without intermediate browser bloat poisoning context.

Long tasks fail when context windows fill with raw page data. Context isolation preserves reasoning accuracy over extended horizons.

11.3 Multi-Step Web State Aggregation

Parallel execution aggregates multiple live web data streams into a single final deliverable.

• Concurrent branching: manager queries weather forecasts, menus, and operating hours simultaneously.
• Targeted browsing: dedicated subagents browse specific restaurant sites like Beach Chalet and Fat Choy.
• State convergence: downstream node arranges open tabs and aggregates final findings.

Branching tasks across independent subagents stops isolated searches from cluttering other workers’ contexts.

Fig 19. Hierarchical subagents gather branch data concurrently before a downstream node aggregates tabs.

11.4 Dynamic Subgoal Decomposition

Managers adapt execution graphs dynamically whenever live web environments reveal missing data.

• Continuous verification: manager checks subagent outputs against user requirements before advancing.
• Dynamic node injection: missing reservation slots prompt the manager to insert subtasks immediately.
• Gantt tracking: monitors subagent runtimes and status transitions to coordinate clean handoffs.

Static workflows break when live websites change unexpectedly. Dynamic graphs let agents recover without restarting runs.

11.5 Cross-Model Multi-Agent Execution

Modular coordination dramatically outperforms monolithic execution on complex desktop workflows.

• 25.0% to 58.3%: success increase on OSWorld by pairing Claude Opus 4.6 with Qwen3.5-4B.
• 25.0% cap: maximum success rate achieved by rigid, static execution graphs.
• Cross-model specialization (Pairing a large semantic model for strategic planning with a lightweight model dedicated to fine-grained, low-level execution): strategic manager directs cheap, fast execution workers.
• Dynamic replanning: in-flight graph modifications unlock substantial accuracy gains over static workflows.

Architecture	Manager	Worker	OSWorld Success
Static DAG	None	Qwen3.5-4B	25.0%
Dynamic MACU	Claude Opus 4.6	Qwen3.5-4B	58.3%

Large planners correct small execution models in flight. This architecture minimizes inference costs while maximizing completion rates.

Fig 20. Pairing an Opus manager with a local Qwen worker boosts OSWorld completion to 58.3%.

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12. MACU Ablations on OSWorld and Odysseys

In short
– Multi-Agent Computer Use splits strategic planning from low-level execution across specialized models.
– Strong managers lift weak local workers from 25.0% to 58.3% success on OSWorld.
– Dynamic graph replanning resolves unexpected environment roadblocks during extended browsing tasks.

12.1 Odysseys ACL Surgeon Case Study

Long-horizon web tasks require coordinating hundreds of discrete GUI actions across diverse websites without losing progress.

• Running example: an agent searching specialized orthopedic surgeons executes 93 click and type actions.
• 93 actions: discrete GUI operations across multiple websites to compile credentials into a spreadsheet.
• Odysseys benchmark: evaluates donated human browsing sessions taking 30 to 60 minutes of interactions.
• Directed Acyclic Graph (DAG) [a dependency-aware graph of subtasks used by a manager agent to coordinate and dispatch parallel worker agents without circular loops]: coordinates sub-agents along topological dependencies without circular loops.
• Manager oversight: Claude Sonnet decomposes user goals and coordinates parallel sub-agent workers.

Dynamic revision steps:
1. Sub-agent completes a step and returns environmental state observations.
2. Manager inspects resulting artifacts to verify task criteria.
3. Manager directs retries or inserts subtask nodes into the dependency graph.

Dynamic graphs prevent circular deadlocks during browser exploration. Decentralized execution keeps long-horizon navigation on track.

12.2 Manager Models and Coordination Quality

Decoupling strategic planning from routine execution reduces token costs while maintaining high task completion rates.

Role	Assigned Model	Token Volume	Primary Responsibility
Manager	Frontier API (e.g., Opus 4.6)	Low context volume, high cost	Strategic decomposition and DAG error checking
Worker	Local model (e.g., Qwen3.5-4B)	Absorbs massive interaction tokens	Discrete GUI groundings, clicks, and keystrokes

• Cross-model specialization [pairing a large semantic model for strategic planning with a lightweight model dedicated to fine-grained, low-level execution]: concentrates expensive reasoning strictly on planning and verification.
• Cost efficiency: lightweight workers absorb continuous context, keeping total financial expense low.
• Frontier guidance: models like Claude Opus 4.6 and GPT-5.4 prevent workers from straying off-task.

Fig 21. Upgrading frontier managers improves coordination success while keeping token expenditure minimal.

Frontier models excel at reasoning but cost too much for raw interaction loops. Specialization balances capability against budget.

12.3 Uplifting Constrained Worker Agents

Structured oversight shields small local models from cascading drift across complex long-horizon benchmarks.

• 25.0% -> 58.3%: OSWorld success rate increase when Claude Opus 4.6 manages Qwen3.5-4B.
• 25.0%: baseline standalone success of Qwen3.5-4B across 36 OSWorld tasks.
• Compounding drift [the exponential accumulation of small, early per-step execution errors that cascades into irreversible failure across long horizons]: prevented by offloading task decomposition to the manager.
• Narrow focus: worker focuses strictly on short-horizon tool calls and local GUI groundings.

Fig 22. Structured manager coordination lifts the benchmark performance of parameter-constrained worker agents.

Small models fail when tracking complex goals across extended trajectories. Manager oversight catches small errors before they cascade.

12.4 Parallelism and Dynamic Graph Revisions

Dynamic DAG replanning allows autonomous agents to navigate runtime obstacles without human intervention.

• 25% cap: ceiling for static DAG success rates on long-horizon tasks.
• Subtask separability: verify whether subtasks decouple cleanly and run independently.
• Model specialization: determine whether planning and execution require distinct model sizes.
• Replanning triggers: establish which environment errors or exceptions initiate graph restructuring.
• Context isolation: partition authority between agents to stop context accumulation across steps.
• Human oversight: identify irreversible operations that mandate manual human verification.

Fig 23. Long-horizon success: a fixed DAG stays near 25%, one replan helps a little, continuous updates help most.

Static execution plans fail when websites present unexpected layouts or data. Dynamic graph revisions inject new subtasks to bypass roadblocks autonomously.

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13. Summary

• Modular specialization: Decoupling strategic planners from lightweight grounded executors cuts inference costs and prevents low-level action failures.
• Interaction scaling: Real-time environment feedback outperforms prolonged internal chain-of-thought deliberation while suppressing analysis paralysis and overthinking pathologies.
• Error mitigation: Reinforcement learning and programmatic subagent delegation isolate context windows to stop compounding drift and autoregressive self-conditioning.
• Staged execution: Upfront programmatic plans and human-in-the-loop markdown approval gates block prompt injections and contain irreversible file mutations.
• Dynamic multi-agent coordination: Frontier manager DAGs dynamically replan worker subtasks across isolated virtual machines, lifting OSWorld completion to 58.3%.

Decoupling high-level planning from grounded execution enables scalable, secure agent architectures that reliably navigate complex long-horizon computer workflows.

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Source

Lecture: Planning, Task Decomposition, and Multi-Agent Coordination by Daniel Fried, Carnegie Mellon University.
Course: CMU 11-768 AI Agents (Fall 2026), Lecture 5, second half. The first half is covered in LLM_log #034.
Video: YouTube · Slides: PDF · Course page: cmu-agents.com

Further reading referenced in the lecture:

• Plan-and-Act · Erdogan et al., ICML 2025 · Figure 3
• Don’t Sleep on Single-agent Systems · Neubig, 2024
• CodeR · Chen et al., 2024 · Figure 1
• SayCan · Ahn et al., CoRL 2022 · Figure 3
• Zero-Shot Planners · Huang et al., ICML 2022
• LLM-Planner · Song et al., ICCV 2023 · Figure 1
• Inner Monologue · Huang et al., CoRL 2022
• SwiftSage · Lin et al., NeurIPS 2023
• Test-Time Interaction · Shen et al., NeurIPS 2025 · Figure 3
• Cuadron et al., 2025 · Figures 1 and 4
• Calibrate-then-Act · Ding, Tomlin, Durrett, 2026 · Figure 1
• PPP-Agent · Sun et al., COLM 2026
• Vending-Bench · Backlund and Petersson, 2025 · Tables 3–4
• Sinha et al., 2025 · Figure 1
• RAO · Gandhi et al., 2026 · Figures 1 and 7
• Recursive Language Models · Zhang et al., 2025
• ADaPT · Prasad et al., NAACL Findings 2024
• Piet et al., 2026 · Figure 1
• Cursor
• Cursor Introducing Plan Mode Introducing Plan Mode
• TravelPlanner · Xie et al., ICML 2024 · Figure 1
• Odysseys · Jang, Koh, Fried, Salakhutdinov, 2026
• MACU · Koh, Salakhutdinov, Fried, 2026

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dataHacker.rs — LLM_log #035: Structuring Agent Planning — Modularity, Affordances, Long Horizons, and Multi-Agent Execution. By Vladimir Matic.