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New projects
A paragraph about the problem is enough to start. We reply within two working days, usually with questions rather than a quote.
AI · Service Multi-agent system development
Multi-agent AI systems with clear roles, durable state, safe hand-offs and evaluation — built like the distributed systems they are.
In short
A multi-agent system splits a large task between several AI agents with different roles — a planner, a researcher, a coder, a reviewer — that pass work to each other. It can handle problems too big or varied for one agent. It also brings the problems of any distributed system: shared state, retries, duplicated actions and deciding who is right when agents disagree. We build multi-agent systems with those problems solved first.
- How to engage
- Product build, Dedicated engineering pod
- Practice
- AI
Signs this is the right service
- One agent with every tool has become slow, expensive or unreliable.
- The task naturally splits into stages handled by different specialists.
- Work must run for minutes or hours and survive failures midway.
- Agents from different teams or vendors need to cooperate.
What you get
Role design
Each agent with a narrow job, its own tools and permissions, and a clear contract for what it hands on.
Orchestration
A supervisor or workflow engine that routes work, enforces limits and keeps the overall plan.
Durable state
Long-running work stored outside the models, so it resumes after a crash without repeating side effects.
Agent-to-agent protocols
Standard protocols such as A2A and MCP where agents and tools come from different systems.
Evaluation
End-to-end tests of the whole system and per-agent tests, so a regression can be traced to its source.
The process
- 01
Start with one agent
We only split into several when one agent measurably cannot do the job.
- 02
Define the hand-offs
What each agent receives and returns, written as typed contracts.
- 03
Build on a workflow engine
Durable execution, retries and timeouts handled by infrastructure, not prompts.
- 04
Evaluate and observe
Traces across agents, cost per run and quality scores on every change.
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Multi-agent systems: common questions
When do we need more than one agent?
- When a single agent's instructions and tools grow too large to be reliable, or the work has distinct stages that benefit from separate expertise. Many tasks are better served by one well-designed agent.
What is A2A?
- Agent2Agent is an open protocol for agents built by different teams or vendors to discover each other and exchange tasks. MCP connects agents to tools; A2A connects agents to agents.
Are multi-agent systems more expensive?
- They can be, because each agent makes its own model calls. We track cost per run and use smaller models for simpler roles.
How do you debug them?
- With a trace that follows a task through every agent, tool call and hand-off, so a failure can be traced to the step that caused it.
06 / Let’s build
Bring us the difficult problem.
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Careers
Engineers, designers and researchers who like systems that are hard to get right. Send something you built and what you would change about it now.
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Studio
Based in India and working remotely, with overlap hours agreed up front so your team always has a time to reach us.
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Read
Guides on AI agents, RAG, MCP, tokenization and chain abstraction, written by the engineers who build them.