We ran 2 vs 4 agents six times. Four agents cost 2.1 and did not improve success

Short version

I ran a preregistered 2-agent versus 4-agent comparison on a deterministic task. Six live runs completed under hard caps. Both groups succeeded in exactly one of three repeats. The 4-agent group cost 2.115× more per run and per complete success. More agents were operationally viable; they were not better on this task.

The task

The scenario is a deterministic public-repair contribution task:

• eight named participants;
• each participant chooses how many repair units to contribute;
• the task succeeds only if the total reaches a fixed threshold;
• participant coverage is forced, so every selected agent acts;
• no LLM reasoning is used for the choice; the model returns a strict JSON choice.
  The point of forcing coverage was to avoid the earlier failure mode where one agent dominated every turn and the other participants never acted.

Protocol

Item	Value
Groups	2 agents / 3 steps; 4 agents / 5 steps
Repeats	3 per group
Caps per run	150 calls / USD 0.50 / 600s
Choice model	deepseek-flash on a Responses API contract
Outcome	machine-decidable outcome.json
Validity	provenance, coverage, integer choices, arithmetic, caps, port closure, archive hashes

All six runs passed every validity gate. No parser failure, no budget breach, no port leak.

Results

Run	Agents	Total / threshold	Success	Zero contributors	Calls	Total tokens	Cost USD
2-agent r1	2	2 / 3	no	1	17	10,060	0.0269116
2-agent r2	2	3 / 3	yes	0	17	11,070	0.0313716
2-agent r3	2	0 / 3	no	2	17	10,255	0.0281116
4-agent r1	4	3 / 5	no	2	33	21,493	0.0597772
4-agent r2	4	2 / 5	no	2	33	22,679	0.0641372
4-agent r3	4	5 / 5	yes	0	33	21,459	0.0588042

Group aggregates

Metric	2-agent	4-agent	Ratio
complete successes	1 / 3	1 / 3	1.000
mean calls	17.00	33.00	1.941
mean total tokens	10,461.67	21,877.00	2.091
mean cost USD	0.028798	0.060906	2.115
mean zero contributors	1.000	1.333	1.333

What I take from it

• More agents are not automatically better. In this task they doubled cost and left more participants contributing nothing on average.
• The result is a valid negative result, not an apparatus failure. Every run completed and every artifact passed its gates.
• The 4-agent group did produce one complete success, so the mechanism is not broken. It is simply not worth 2.115× the cost on this task.

What this does not show

• It does not show that four agents are worse in general.
• It does not transfer to another task or to a real user.
• It says nothing about agent quality under human review, because no human scored these runs.
• The sample is three repeats per group on one synthetic task.

Reproduce it

Two pieces are now public:

• Method package: https://github.com/janzong/agent-lab-method (MIT, commit 6cc70156facb37baf23fe5fe57dad93d43502b91) - schema, synthetic GenMentor adapter, fixtures, tests;
• Trust layer: https://github.com/janzong/agent-lab-trust (MIT, release v0.1.0-rc2) - local-first run validation, hashed reports, and a synthetic deletion proof.
  The full protocol and the archived runs stay private. The result is intentionally boring: a negative result with caps, hashes, and every failed run preserved. I am also looking for three independent reproductions by non-authors. The trust layer guide expects 13 passed under both TZ=UTC and TZ=Asia/Shanghai, report_hash a841b192981fd7e7, and deletion audit_hash 4f0193abbd49a0f9. If you run it and any hash differs, that is the most useful reply I can get. If you have a task where you believe more agents should win, that is the experiment I want to run next.

Short version

I ran a preregistered 2-agent versus 4-agent comparison on a deterministic task. Six live runs completed under hard caps. Both groups succeeded in exactly one of three repeats. The 4-agent group cost 2.115× more per run and per complete success. More agents were operationally viable; they were not better on this task.

The task

The scenario is a deterministic public-repair contribution task:

• eight named participants;
• each participant chooses how many repair units to contribute;
• the task succeeds only if the total reaches a fixed threshold;
• participant coverage is forced, so every selected agent acts;
• no LLM reasoning is used for the choice; the model returns a strict JSON choice.
  The point of forcing coverage was to avoid the earlier failure mode where one agent dominated every turn and the other participants never acted.

Protocol

Item	Value
Groups	2 agents / 3 steps; 4 agents / 5 steps
Repeats	3 per group
Caps per run	150 calls / USD 0.50 / 600s
Choice model	deepseek-flash on a Responses API contract
Outcome	machine-decidable outcome.json
Validity	provenance, coverage, integer choices, arithmetic, caps, port closure, archive hashes

All six runs passed every validity gate. No parser failure, no budget breach, no port leak.

Results

Run	Agents	Total / threshold	Success	Zero contributors	Calls	Total tokens	Cost USD
2-agent r1	2	2 / 3	no	1	17	10,060	0.0269116
2-agent r2	2	3 / 3	yes	0	17	11,070	0.0313716
2-agent r3	2	0 / 3	no	2	17	10,255	0.0281116
4-agent r1	4	3 / 5	no	2	33	21,493	0.0597772
4-agent r2	4	2 / 5	no	2	33	22,679	0.0641372
4-agent r3	4	5 / 5	yes	0	33	21,459	0.0588042

Group aggregates

Metric	2-agent	4-agent	Ratio
complete successes	1 / 3	1 / 3	1.000
mean calls	17.00	33.00	1.941
mean total tokens	10,461.67	21,877.00	2.091
mean cost USD	0.028798	0.060906	2.115
mean zero contributors	1.000	1.333	1.333

What I take from it

• More agents are not automatically better. In this task they doubled cost and left more participants contributing nothing on average.
• The result is a valid negative result, not an apparatus failure. Every run completed and every artifact passed its gates.
• The 4-agent group did produce one complete success, so the mechanism is not broken. It is simply not worth 2.115× the cost on this task.

What this does not show

• It does not show that four agents are worse in general.
• It does not transfer to another task or to a real user.
• It says nothing about agent quality under human review, because no human scored these runs.
• The sample is three repeats per group on one synthetic task.

Reproduce it

Two pieces are now public:

• Method package: https://github.com/janzong/agent-lab-method (MIT, commit 6cc70156facb37baf23fe5fe57dad93d43502b91) - schema, synthetic GenMentor adapter, fixtures, tests;
• Trust layer: https://github.com/janzong/agent-lab-trust (MIT, release v0.1.0-rc2) - local-first run validation, hashed reports, and a synthetic deletion proof.
  The full protocol and the archived runs stay private. The result is intentionally boring: a negative result with caps, hashes, and every failed run preserved. I am also looking for three independent reproductions by non-authors. The trust layer guide expects 13 passed under both TZ=UTC and TZ=Asia/Shanghai, report_hash a841b192981fd7e7, and deletion audit_hash 4f0193abbd49a0f9. If you run it and any hash differs, that is the most useful reply I can get. If you have a task where you believe more agents should win, that is the experiment I want to run next.

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