Deep dive · · 6 min read
Adding a second agent does not add intelligence, it adds a contract
Six posts on multi-agent systems, and the failures were never inside an agent. They were between two of them.
Topic
In the glossary: Orchestration (supervisor, pipeline, peer), what each means and how to say it in a review
Part of AI & Models: all AI & Models entries · the AI & Models lens on the map
Deep dive · · 6 min read
Six posts on multi-agent systems, and the failures were never inside an agent. They were between two of them.
Deep dive · · 6 min read
The topology gets all the design attention. Ownership, termination and traceability are what decide whether it survives contact with production.
Deep dive · · 6 min read
Six days of notes on supervising autonomous systems, and the same failure shape kept turning up: the control exists, it is documented, and nothing in the running system is bound by it.
Explainer · · 2 min read
Multi-agent systems need trace timelines, trajectory evals and designed recovery, because the final output hides everything that produced it.
Explainer · · 2 min read
Permissions belong to roles, budgets belong to runs, and termination has to be something a machine can check.
Explainer · · 2 min read
Supervisor, orchestrator-worker, planner-executor, pipeline and judge differ mainly in where control lives and when it returns.
Explainer · · 2 min read
"You are a meticulous senior researcher" is a costume. Inputs, outputs, permissions and done criteria are a contract.
Comparison · · 2 min read
Separation of work has to buy quality, control or throughput. Usually it buys a harder debugging problem.
Explainer · · 1 min read
Why agents need arbitration rules before disagreement reaches the final answer.
Explainer · · 2 min read
Why agents need shared message contracts before they can collaborate reliably.
Explainer · · 2 min read
Why agent roles need clear purpose, authority, tools, memory, and evaluation.
Explainer · · 1 min read
Why decentralized agent behavior needs convergence rules before it earns production trust.
Explainer · · 2 min read
Why AI debate is only useful when the judge has reliable criteria.
Explainer · · 1 min read
Why shared state needs structure, attribution, and versioning to stay debuggable.
Explainer · · 1 min read
Why peer agents need communication rules, round limits, and a final decision owner.
Explainer · · 2 min read
Why dynamic dispatch needs visible routing decisions and clear final ownership.
Explainer · · 1 min read
Why predictable AI workflows often benefit from pipeline structure before agent autonomy.
Explainer · · 1 min read
Why supervisor agents help only when routing, review, and authority are clear.
Explainer · · 1 min read
Why hierarchy helps when work has real layers of authority and responsibility.
Explainer · · 1 min read
Why multi-agent systems should reduce complexity through specialization, not add agents for novelty.