Can AI Models Direct Each Other? Organizational Structure as a Probe into Training Limitations

Rui Liu

cs.SEMar 27, 2026

Can AI Models Direct Each Other? Organizational Structure as a Probe into Training Limitations

Authors: Rui Liu

Organizations: Independent Researcher

Abstract

Can an expensive AI model effectively direct a cheap one to solve software engineering tasks? We study this question by introducing ManagerWorker, a two-agent pipeline where an expensive "manager" model (text-only, no code execution) analyzes issues, dispatches exploration tasks, and reviews implementations, while a cheap "worker" model (with full repo access) executes code changes. We evaluate on 200 instances from SWE-bench Lite across five configurations that vary the manager-worker relationship, pipeline complexity, and model pairing. Our findings reveal both the promise and the limits of multi-agent direction: (1) a strong manager directing a weak worker (62%) matches a strong single agent (60%) at a fraction of the strong-model token usage, showing that expensive reasoning can substitute for expensive execution; (2) a weak manager directing a weak worker (42%) performs worse than the weak agent alone (44%), demonstrating that the directing relationship requires a genuine capability gap--structure without substance is pure overhead; (3) the manager's value lies in directing, not merely reviewing--a minimal review-only loop adds just 2pp over the baseline, while structured exploration and planning add 11pp, showing that active direction is what makes the capability gap productive; and (4) these behaviors trace to a single root cause: current models are trained as monolithic agents, and splitting them into director/worker roles fights their training distribution. The pipeline succeeds by designing around this mismatch--keeping each model close to its trained mode (text generation for the manager, tool use for the worker) and externalizing organizational structure to code. This diagnosis points to concrete training gaps: delegation, scoped execution, and mode switching are skills absent from current training data.

Figures & tables

Panel of Figure 1 — Figure 1: The ManagerWorker pipeline. The manager (top lane, text-only) analyzes, reviews, and plans. Workers (bottom lane, full repo access) explore and implement. Loop arrows on the left side show iterative rounds. Communication is structured natural language.

Configuration	Description	Manager	Worker	Pipeline
ManagerWorker	Full structured pipeline	Sonnet 4.6	GPT-5-mini	5-phase
Simple Loop	Minimal review loop	Sonnet 4.6	GPT-5-mini	execute $\to$ review
Strong Direct	Single strong agent	—	Sonnet 4.6	none
Weak Direct	Single weak agent	—	GPT-5-mini	none
Weak $\to$ Weak	Weak manager, weak worker	GPT-5-mini	GPT-5-mini	5-phase

Table 1: Experimental configurations. Direct configurations are single-agent baselines. ManagerWorker and Simple Loop test two levels of pipeline complexity with the same model pairing. Weak

\to

Weak tests whether structure helps without a capability gap.

Configuration	Manager	Worker	$n$	Resolved	Rate
ManagerWorker (full pipeline)	Sonnet 4.6	GPT-5-mini	200	124/200	62%
Strong Direct	—	Sonnet 4.6	200	120/200	60%
Simple Loop	Sonnet 4.6	GPT-5-mini	200	106/199	53%
Weak Direct	—	GPT-5-mini	200	101/200	51%
50-instance subset (for Weak $\to$ Weak comparison):
ManagerWorker (full pipeline)	Sonnet 4.6	GPT-5-mini	50	32/50	64%

Table 2: Main results. Top: 200-instance evaluation. ManagerWorker matches Strong Direct (+2pp) using primarily cheap worker tokens. Bottom: 50-instance subset including Weak

\to

Weak, which performs worse than Weak Direct (42% vs 44%)—structure without a capability gap is net negative.

Configuration	Resolve	Strong tokens	Weak tokens	Total tokens
ManagerWorker (full)	62%	$\sim$ 6.6K	$\sim$ 60K	$\sim$ 67K
Strong Direct	60%	$\sim$ 30K	0	$\sim$ 30K
Simple Loop	53%	$\sim$ 3K	$\sim$ 30K	$\sim$ 33K
Weak Direct	51%	0	$\sim$ 15K	$\sim$ 15K
Weak $\to$ Weak	42%	0	$\sim$ 75K	$\sim$ 75K

Table 3: Quality–cost tradeoff across configurations. Token estimates per instance based on typical prompt sizes and session lengths. Resolve rates for top four configs from

n=200

; Weak

\to

Weak from

n=50

. ManagerWorker achieves comparable quality to Strong Direct while shifting

\sim

90% of tokens to the cheap model. Weak

\to

Weak is the worst of both worlds: high total tokens and low quality.

Batch	Resolved	Rate	Notes
0–50	26/49	53%	Initial pipeline
50–100	30/51	59%	+ code context in reviews
100–150	33/52	63%	+ relaxed execution constraints
150–200	17/47	36%	Same pipeline as 100–150; harder instances

Table 4: Simple Loop (v2) performance by batch. Batches 50–150 benefited from both pipeline improvements and easier instances, so these rates are confounded. The clean comparison is batch 100–150 (63%) vs 150–200 (36%): same pipeline, harder instances. This 27pp drop exposes the ceiling of the review-only approach.

Combination	Resolved
ManagerWorker	32/50 (64%)
Strong Direct	29/50 (58%)
Union	33/50 (66%)
Intersection	28/50 (56%)
ManagerWorker only	4/50 (8%)
Strong Direct only	1/50 (2%)

Table 5: Complementarity analysis on 50-instance subset. ManagerWorker resolves nearly all instances that Strong Direct resolves (28/29), plus 4 additional. The approaches solve largely overlapping but not identical subsets.

Manager Configuration	Resolved (toy-5)	Key Change
v5–v6: Manager with repo access	0–1/5	Manager hallucinates “already fixed”
v7: Manager text-only	2/5	Forced abstract reasoning
v9: Text-only + iterative exploration	3/5	Manager can request more info

Table 6: Director design ablation on a 5-instance development set. Giving the manager repository access caused a regression—the model reverted to its training behavior (read files, form conclusions) rather than delegating investigation.

Strategy	Resolved	Empty	Eval Err	Description
Strict only	26/50 (52%)	8	6	Worker follows plan exactly; no autonomy
Guided only	27/50 (54%)	5	1	Worker has full autonomy on every round
Guided-then-strict	32/50 (64%)	0	2	Autonomy on round 1, strict on retries

Table 7: Communication strategy ablation on 50-instance subset. The guided-then-strict strategy produces the largest improvement (+10pp over strict-only), showing that cheap models need different levels of constraint at different stages.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Solved by ManagerWorker only	Solved by Strong Direct only
matplotlib-23299	astropy-14365
matplotlib-23563
matplotlib-23987
seaborn-3190
sympy-12171

Appendix

Table 8: Instances uniquely solved by each approach. 5 unique to ManagerWorker , only 1 unique to Strong Direct. The manager-worker pipeline subsumes nearly all of the single agent’s capability.

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