Multi-agent orchestration development

Systems where multiple AI agents coordinate on complex workflows — task routing, hand-offs, shared state, and human checkpoints — built and evaluated end-to-end.

What we build

For workflows too complex for a single assistant: several specialized AI agents coordinating — one triaging, one assessing, one drafting — with defined hand-offs, shared state, and human checkpoints at the decisions that matter. Built and proven with an end-to-end evaluation suite. The right choice when a process has distinct stages that each need their own expertise.

What you get

• The orchestration system's source code in a Git repository
• A hand-off diagram naming each agent, its inputs and its exit conditions
• A trace view showing each step of a completed multi-agent run
• An end-to-end evaluation suite scoring complete multi-stage runs

How the work unfolds

1. Design the agent topology and handoffs
2. Build the orchestration layer
3. Add human sign-off gates between agents
4. Trace and debug cross-agent flows
5. Evaluate end-to-end quality

What shapes the price

Before you see a number, our scoping conversation asks:

• How many distinct stages does the process have — steps that today are handled by different people or teams? Each stage is an agent with its own inputs, exit conditions and failure handling.
• Should a person approve the work before it moves from one stage to the next? Human sign-off gates between agents are the priciest optional activity here.

How an engagement starts

This work builds on AI agent development, so scope, boundaries and constraints are agreed before anything is built.

Teams often combine it with:

• LLM evaluation suite