Agent swarms that stay accountable.
A swarm multiplies whatever you already have — including the trust problem. A hundred agents sharing one set of keys and one process is a hundred times harder to audit and a hundred times more to lose control of. a2a cloud makes each swarm member a distinct deployed agent: its own identity, its own scoped grant, its own isolated database, its own signed receipts, and its own scale-to-zero. Swarm throughput, member-level accountability.
named members · grant per role · replay the whole swarm
A swarm of anonymous workers is a swarm you can't govern.
The appeal of a swarm is parallelism — many agents chewing on a problem at once. The failure mode is anonymity: when members are threads inside one process, sharing one credential and one database, you lose the ability to say which one acted, under what authority, or how to stop it. Debugging becomes archaeology, security becomes all-or-nothing, and cost becomes a single opaque number. Scale made the system faster and the accountability worse.
Make every member a first-class, signed, scoped agent.
a2a gives each swarm member the same primitives a standalone agent gets — identity, grant, database, receipts, scale-to-zero — so parallelism doesn't cost you accountability.
Every swarm member is named
Each agent in the swarm is a deployed actor with its own card and identity — not an anonymous worker. When something happens, the receipt names the exact member, not 'the swarm'.
Scoped grants per member
A member holds only the audience, tools, files, and TTL its role needs. A hundred agents don't mean a hundred copies of your keys — they mean a hundred narrow, revocable grants.
The whole swarm is replayable
Every member's runs are Ed25519-signed and hash-chained, so you can reconstruct the entire swarm's behavior — who called whom, in what order, with what result — and replay it deterministically.
Scale-to-zero per member
Idle members cost nothing. Each agent scales to zero independently, so a large swarm that mostly waits doesn't bill like a large swarm that's always on.
Anonymous swarm vs. governed swarm.
Frequently asked.
What is an AI agent swarm?
An agent swarm is a group of agents that work in parallel on a shared goal, coordinating by passing tasks and results between one another rather than following a single linear script. The value is throughput and redundancy; the risk is that a swarm of anonymous workers is nearly impossible to audit. a2a cloud makes each swarm member a distinct deployed agent with its own identity, grant, and receipt chain, so the swarm stays accountable at member granularity.
How do you audit which member of a swarm did what?
Every run by every member emits an Ed25519-signed, hash-chained receipt naming the member, its inputs and outputs, the tool calls it made, the authorizing grant, and the cost. Because members are distinct deployed identities rather than threads, an incident resolves to a specific agent and a specific run — and deterministic replay lets you re-execute the exact sequence to see how the swarm reached a result.
Won't running a large swarm be expensive?
Each member scales to zero independently, so idle agents cost nothing. A swarm that spends most of its time waiting on a few active branches bills for the active work, not for keeping every member warm. Per-member isolated Postgres and per-run cost attribution also let you see exactly where the swarm's spend actually goes.
Related guides.
All guides live in the guides index.
Run the swarm. Keep the receipts.
a2a cloud deploys any agent — LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or custom — and every member of your swarm gets an isolated Postgres database, an MCP server, an API, a scoped grant, and an Ed25519-signed receipt for every run. Parallel by design, accountable by member.