Multi-Agent Systems
A team of digital operators, not one chatbot.
Fleets of specialised AI agents that hand work to each other — one qualifies, one drafts, one updates the record — coordinated with shared memory, guardrails, and human approval where it counts.
A single all-purpose bot is a demo. It works in the happy path and falls apart the moment a real request arrives with three intentions in one sentence. The fix isn't a longer prompt, it's an architecture.
We build fleets of narrow agents, each with one job, one set of tools, and one definition of done — coordinated by a router that decides who handles what and escalates when confidence drops. Every action is logged, every sensitive step is gated behind a human, and outputs are evaluated continuously so quality doesn't quietly drift.
How this build actually runs
Every stage has a visible output. You always know which node we're on.
Role decomposition
We split the job into discrete roles — qualifier, researcher, drafter, reviewer, executor — each with a scope narrow enough to test.
Orchestration layer
A router classifies intent and dispatches to the right agent, with fallbacks and a confidence threshold that triggers human review.
Memory & knowledge
Shared context plus a retrieval layer over your real documents, so agents answer from your truth rather than guessing.
Guardrails & approvals
Allowed-action lists, spend and send limits, redaction of sensitive fields, and approval gates on anything irreversible.
Continuous evaluation
A scored test suite runs against every change, plus review of real transcripts to catch drift before customers do.
What lands in your hands
What changes
Typical shapes this takes
Inbound qualified, researched, and drafted before a rep opens the thread.
Tickets triaged, summarised, answered from real docs, escalated with context.
Research, drafting, and on-brand review as three separate reviewed steps.
What people ask before starting
Not if it's scoped properly. Agents get explicit allowed-action lists, hard limits on spend and sending, and approval gates on anything irreversible.
A chatbot answers. An agent fleet acts — reading your systems, taking steps, handing work between specialised roles, and escalating when it should.
Whichever fits the task and budget, per agent. Cheap fast models for classification, stronger models for reasoning and drafting.