Multi-agent systems that finish the job
One agent answers questions. A coordinated team of agents plans, divides the work, executes across your systems, and checks itself — with a human on the approvals that count.
- Orchestrator + scoped workers
- Shared memory & typed handoffs
- Supervised, budgeted runs
- Full per-agent decision lineage
A team beats a polymath
Multi-agent design is software architecture, not prompt magic.
Cram every responsibility into one agent and you get a brilliant intern who forgets the first instruction by the tenth, reaches for the wrong tool, and can't tell you why. The fix isn't a bigger model — it's division of labor.
We build systems where a planner breaks a goal into discrete tasks, specialist workers own one domain each (research, retrieval, drafting, validation, system writes), and a supervisor watches the whole run for cost, quality, and drift. Each agent stays small, sharp, and accountable.
The result behaves less like a chatbot and more like a well-run operations team: clear roles, explicit handoffs, and a paper trail for every decision.
What's inside a system we ship
Each role is a separate, observable component — never a monolith you can't reason about.
Orchestrator
Decomposes the goal, sequences the work, routes each task to the right specialist, and re-plans when a step fails.
Specialist workers
Narrow agents that each own one job — tight prompts, a minimal toolset, and a context window that stays clean.
Shared memory
A common state layer plus retrieval over your sources, so agents build on each other instead of re-deriving facts.
Approval checkpoints
Human-in-the-loop gates on irreversible actions, with the full plan and reasoning surfaced for a one-click decision.
Decision lineage
Every task, tool call, and handoff is logged per agent — so any outcome can be traced, replayed, and explained.
Budgets & supervision
Step ceilings, token caps, timeouts, and a watchdog that halts loops before they cost you anything.
How a request flows through the team
From a single goal to completed work in your systems.
Plan
The orchestrator reads the goal, pulls context from memory, and decomposes it into a typed task graph.
Delegate
Each task goes to the specialist built for it; workers run in parallel where the graph allows.
Verify
A reviewer agent checks outputs against acceptance criteria and bounces anything that fails back upstream.
Commit
Reversible steps execute autonomously; high-stakes ones pause for human approval, then write to your systems.
Structured handoffs, not a group chat
The failure mode of naive multi-agent setups is agents talking endlessly to each other in free text — drifting off task, contradicting one another, and quietly burning your budget. We don't build those.
Our agents communicate through typed contracts: the orchestrator issues a task with explicit inputs and a defined output shape, and the worker returns structured data. Shared state is the single source of truth, so coordination is deterministic enough to test, log, and replay.
- Typed task contracts between agents
- Structured outputs, not free-form chatter
- Replayable runs you can debug like code
One mega-agent vs. an orchestrated team
Why we almost always reach for a system of specialists.
| A single mega-agent | An orchestrated team | |
|---|---|---|
| Context | One bloated prompt, easily lost | Small, scoped context per agent |
| Tools | Dozens — frequent wrong picks | A handful per specialist, used well |
| Failure | Opaque; hard to localize | Traceable to one agent and step |
| Scaling | Add tools, lose reliability | Add specialists, keep reliability |
| Oversight | All-or-nothing | Gates placed exactly where risk lives |
Frequently asked questions
Why use multiple agents instead of one big one?
A single agent with thirty tools and a sprawling prompt degrades fast — it loses the plot, picks the wrong tool, and is nearly impossible to debug. Splitting the work into scoped specialists keeps each prompt tight, each context small, and each failure traceable to one agent.
How do the agents actually talk to each other?
Not by chatting freely. An orchestrator decomposes the goal and hands typed tasks to workers; results return as structured payloads, not prose. State lives in shared memory both can read, so coordination is explicit and auditable rather than an emergent free-for-all.
What stops a multi-agent system from looping or burning tokens?
Hard budgets. Every run has step ceilings, per-agent token caps, timeouts, and a supervisor that kills stalled or oscillating branches. Cost and latency are first-class metrics on the dashboard, not a surprise on the invoice.
Where does a human fit into a system this automated?
At the seams that matter. High-stakes handoffs — sending money, emailing a customer, changing a record of truth — pause for approval. Everything reversible runs unattended. You set the line, and you can move it as the system earns trust.
Have a goal that needs more than one agent?
Bring the workflow that keeps getting stuck. We'll sketch the agent team, the handoffs, and where the human stays in the loop.