When one agent is not enough, when the workflow has a planner, a researcher, a writer, and a reviewer, you need an orchestrated multi-agent system, not a chain of prompts. Each specialist agent handles one clearly scoped function. A supervisor coordinates the graph and routes decisions to a human when an action cannot be undone.
Who this is for
Engineering leaders past the proof-of-concept stage who have hit the ceiling of single-agent reasoning. Operations leaders running multi-step processes where different sub-tasks need genuinely different capabilities. Founders building category-defining products that cannot be solved by a single LLM call.
What you get
- A multi-agent graph in LangGraph or equivalent, with each agent’s scope documented explicitly.
- Supervisor, worker, and specialist patterns matched to your workflow’s decision structure.
- Shared memory with conflict resolution so agents do not overwrite each other’s state.
- Per-agent eval suites with accuracy targets defined before build begins.
- A cost dashboard with per-agent attribution so you know where your token spend goes.
- Full deployment documentation and a handover session for your engineering team.
How we work on this
Discovery week establishes the architecture proposal. Then we build a thin-slice prototype, harden it for production, and ship. We provide daily updates throughout.
Tech stack
LangGraph for state machines with branching logic. Pydantic AI for type-safe agent contracts. LangSmith for tracing the full graph execution. MCP for every external tool the agents need to reach.
When this is the wrong choice
If a single well-prompted agent works, use that. Multi-agent overhead is real: more tokens, more latency, and more failure surface. We recommend multi-agent architecture only when the workflow genuinely requires it.
Pricing
Three tiers: $15,000 for up to 3 agents with straightforward coordination. $25,000 for 4 to 6 agents with custom memory and routing. $45,000 for complex graphs with 7-plus agents, custom MCP servers, and full production hardening.
FAQ
How do you control costs? Each agent runs on the cheapest model that meets its accuracy requirement. We set per-agent token budgets and log overruns from day one.
What is the latency impact of a multi-agent graph? It depends on how much runs in parallel versus in sequence. We design for maximum parallelism where the workflow allows. Most graphs complete in 15 to 45 seconds for end-to-end tasks.
How do you debug when something goes wrong? LangSmith gives us full execution traces for every graph run. We can replay any failing trace against a fixed checkpoint to reproduce and fix the issue.
How do you choose which model runs each agent? We match model capability to the agent’s task. A reasoning-heavy planner might run on Claude Opus 4.7, while a classification agent runs on Haiku 4.5 at a fraction of the cost.
Who handles maintenance after handover? Every build ships with a runbook and eval scripts. Your engineering team can run the evals on any code change to verify behavior has not drifted.