Multi-agent systems,
only where one agent is not enough.
Orchestrator and specialist agents with per-agent permission boundaries and a separate evaluator — designed against the failure modes multi-agent systems actually have.
Only where one agent
is not enough.
Multi-agent architecture adds real coordination overhead and new failure modes — compounding errors, unclear ownership on failure, silent retry loops. It's justified when a task genuinely splits into independent sub-tasks needing different context or tools, not because it sounds more sophisticated than a single agent.
Start with AI agent development if you're not yet sure a single well-scoped agent won't do the job — most real workloads don't need this page.
Orchestrator,
specialists, and an evaluator.
The evaluator stays separate from the agents it scores — an agent that marks its own work is a known failure pattern. Permission boundaries are set per agent, not inherited from the orchestrator wholesale.
What we design
against.
| Failure mode | Design response |
|---|---|
| One agent's bad output compounds through the next | Validation at each handoff, not just at the end |
| A task gets stuck with no clear owner | Explicit delegation with timeouts and dead-letter handling |
| Silent infinite retry loops | Retry limits with escalation to a human on exhaustion |
| An agent exceeds its intended scope | Per-agent permission boundaries, not inherited wholesale |
What agents share,
and what they don't.
Shared context is where multi-agent systems most often go wrong silently — one agent writes something another reads before it's finalized, or a retried step duplicates a side effect no one scoped for.
MCP and A2A,
where relevant.
We integrate standard agent-to-agent and tool protocols where the use case calls for it, rather than building a proprietary coordination layer without a specific reason to.
Each agent,
and the whole task.
Each agent is scored against its own task. The system is separately scored on whether the overall task actually completed — a chain of individually-passing agents can still fail the real goal. See AI agent testing & evaluation for how we build that harness.
Where it
genuinely fits.
Specialist agents gathering, verifying, and synthesizing from different sources.
Separate agents for sourcing, comparison, and approval routing.
A triage agent delegating to specialists for billing, technical, and account issues.
Agents for code generation, review, and test execution working in sequence.
No sales pitch,
just how we work.
A single agent is simpler and more reliable when it's enough.
No agent marks its own work.
Dead-letter handling and escalation are part of the architecture.
You own the orchestration code and configuration at completion.
Tools we've shipped
in real projects.
Before you
book a call.
When do we actually need multiple agents instead of one?
When a task genuinely splits into independent sub-tasks needing different context or tools — not because multi-agent sounds more sophisticated. A single well-scoped agent handles most real workloads.
What goes wrong with multi-agent systems specifically?
Compounding errors across agents, unclear ownership on failure, and silent infinite-retry loops are the most common failure modes.
How do you evaluate a multi-agent system?
Both individually and end-to-end — each agent scored on its own task, and the system scored on whether the overall task completed.
Do you support MCP or A2A integrations?
Yes, where relevant — we integrate standard protocols rather than building a proprietary one without reason.
What do agents actually share with each other?
An explicitly defined shared-memory schema, not raw working context — one agent's half-finished reasoning can't leak into another's decision. Handoffs between agents are versioned messages, which also means a retry replays the original input rather than a state that's since changed underneath it.
Can a retried step cause a duplicate action, like sending an email twice?
No — any agent step that calls an external system carries an idempotency key, so a retried orchestration step can't duplicate the side effect. This is the same discipline we apply in AI integration work generally, just enforced per agent here.
Tell us why
one agent isn't enough.
Book a 30-minute call — we'll tell you honestly if a simpler architecture would serve you better.