Multi-Agent Systems
Multi-agent system development
One large agent asked to do six things is hard to trust and harder to debug. When it fails you cannot tell which part failed, and every fix risks the parts that were working.
We build several narrow agents instead, each with a single job and a clear success condition. A data cleanse agent, an intake classifier, an analyst. Failures are attributable and cheap to fix, and you can add capability without destabilising what already runs.
What you get
- Specialised agents scoped to one job each
- Email, call, and document analysts with structured output
- CRM intelligence: cleansing, enrichment, classification, tagging
- Transcript-grounded extraction with no fabricated fields
- Scheduled orchestration with daily observation
- Structured output into your existing systems
How it works
- 01Split the workWe break the job into units small enough that each agent has one responsibility and an obvious pass or fail.
- 02Ground each agentAgents answer from the material in front of them. An empty field beats an invented one, because a fabricated value looks real and gets acted on.
- 03OrchestrateScheduling, sequencing, and the handoffs between agents, with the output landing in your systems.
- 04Observe dailyMonitored runs so a failure is attributable to one agent and fixable without touching the rest.
Common questions
Why several small agents instead of one big one?
Attribution. When one agent does six things and the output is wrong, you cannot tell which part broke, and every fix risks the parts that worked. Narrow agents each have a clear success condition, so a failure points at one place.
How do you stop agents inventing data?
We ground them in the source and require them to leave a field empty rather than guess. In extraction work a fabricated value is worse than a missing one, because it looks exactly like a real value and gets acted on downstream. An empty field is a visible prompt for a person to check.
Which models do you use?
Whichever fits the job. In practice that means Claude and OpenAI models chosen per task rather than standardising on one, because they are not equally good at the same things and the right choice changes as models change.
How do we know it is still working?
Monitored schedules with daily observation. Agent output degrades quietly, and a system that stopped working looks identical to one with nothing to do unless you are watching.
Works in