Which frameworks do you build on?

Primarily LangGraph for stateful, controllable agent graphs; we also work with the OpenAI and Anthropic native agent tooling and evaluate CrewAI or AutoGen where they fit. The orchestration and evaluation layers are framework-aware but the governance and tool layer is portable.

How do you stop agents from looping or running up cost?

Hard limits on steps and recursion, per-run token budgets, model routing (cheap models for cheap sub-tasks), and a supervisor that can terminate. Every run is traced with cost and latency so runaway behavior is caught in evaluation, not on your bill.

How does this connect to MCP and knowledge graphs?

Agents call your systems through MCP servers for governed, audited tool access, and retrieve grounded facts from a knowledge graph via GraphRAG. Multi-agent orchestration is the reasoning layer on top of that governed foundation.

How do you keep a multi-agent system reliable in production?

The same discipline as the rest of our AI work: a golden evaluation set built during discovery, a critic gate on every release, tracing on every action, and regression tests that block deploys. Reliability is engineered in, not hoped for.

How do you scope and price a multi-agent engagement?

As a fixed-scope production pilot: we agree the workflow, the acceptance criteria, and the reliability targets up front, then build and ship it in a defined window (typically 6 to 8 weeks) backed by our guarantee — hit the agreed targets or the pilot is free. Broader multi-workflow programs scale from there. We scope precisely in the architecture briefing rather than quoting a rate card.

AI & Machine Learning

Multi-Agent Systems for Enterprise

Orchestrate planner, researcher, tool-caller, and critic agents into workflows one model can't do alone. Built on LangGraph, governed by policy, and auditable end to end.

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What are multi-agent systems?

The Answer

Multi-agent systems are AI architectures where several specialized agents — a planner, researchers, tool-callers, a critic — collaborate to complete a task one model can't reliably do alone. Built on frameworks like LangGraph, each agent has a scoped role, bounded tools, and a shared state, with a supervisor that routes work and a critic that checks output before it ships. The result is more reliable, auditable automation for complex enterprise workflows.

What you get

Three outcomes we commit to before we start.

Specialized agents, one supervisor

A supervisor routes work to scoped agents (plan, retrieve, act, critique) instead of asking one prompt to do everything. Each agent is small, testable, and replaceable, which is what makes the system debuggable in production.

A critic gate before anything ships

An evaluator agent checks each result against your acceptance criteria before it reaches a user or a downstream system. Failures loop back for revision rather than silently shipping a wrong answer.

Governed tools and full traces

Every agent action runs through governed tools (often via MCP), with least-privilege scoping and a complete reasoning-and-action trace. When something goes wrong, you can see exactly which agent did what and why.

The Guaranteed Production Pilot

Fixed scope · Written target

A production Multi-Agent Systems system in your VPC: audited, documented, owned by your team.

Not a slide deck and not a sandbox demo: a working Multi-Agent Systems deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.

Speed

Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.

Zero effort

Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.

Risk reversal

Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.

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