Multi-Agent SystemDevelopment
Several specialised agents that hand work to each other, with a reviewer in the loop, for jobs too broad for one prompt to do well.
Multi-Agent System Development: what the work involves
A single agent asked to research a market, write a report, check its claims and format it for the client tends to do all four mediocrely. Its context fills up, instructions compete, and errors in one stage are invisible to the next. Teams then split the job by hand, passing outputs between prompts in a document, which is slow and easy to get wrong.
We split the work the way a small team would. A planner breaks the request into parts, specialist agents handle research, drafting, data checks or code, and a reviewer agent compares the result with the brief and with source evidence before anything reaches you. Agents communicate through a shared state object, not free chat, so each handoff is inspectable. We use frameworks like LangGraph or plain Python state machines, giving each agent its own tools and a smaller model where possible. Humans approve at defined checkpoints, and we compare the multi-agent design against a single-agent baseline before accepting the extra complexity.
Core features
Role-specific agents
Planner, researcher, writer and reviewer each get focused instructions and only the tools their role needs.
Explicit shared state
Agents pass structured results through a defined state, so every handoff can be read and replayed.
Independent review step
A separate reviewer checks claims against sources and the original brief, catching errors the writer missed.
Human checkpoints
You decide where a person must approve, such as after the plan or before anything leaves the building.
Per-agent model choice
Cheaper models take routine steps and stronger ones handle reasoning, which keeps cost per task manageable.
Baseline comparison
We measure the design against a simpler single-agent version on your tasks, so complexity must prove its worth.
What we get right before launch
More parts, more failure points
Each extra agent adds latency, cost and ways to go wrong. We start with the smallest team that works and add roles only when evaluation shows a clear gain.
Agents agreeing on mistakes
Several models reading the same wrong source can confirm each other. Reviewers are required to cite evidence, and uncertain claims are marked for human verification.
Debugging a conversation
When a result is wrong, you need to know which agent erred. Structured traces with each step's inputs and outputs make that findable without guesswork.
Tools and technology
- LangGraph
- Anthropic Claude
- OpenAI
- Python
- FastAPI
- pgvector
- PostgreSQL
- Redis
Common questions, answered
Do we really need multiple agents?
Often not. Many tasks work fine with one well-designed agent or a fixed pipeline. We only recommend several agents when evaluation shows it improves quality enough to justify the added cost and complexity.
How long does a task take to run?
Longer than a single call, since stages run in sequence and sometimes wait for human approval. We design for background execution with notifications instead of making someone watch a spinner.
Can humans step in midway?
Yes. Checkpoints pause the run and show the current state. You can edit the plan, correct a draft, or stop the task, and the agents continue from your changes.
How is quality measured?
We keep a set of representative tasks with reference outputs or rubrics, score each run, and compare variants. Changes that lower scores are rejected before reaching your team.
More AI Agents & Automation services
All AI Agents & Automation servicesAI Agent Development
An agent that takes a goal, decides which of your tools to use, completes the steps, and asks for approval before anything consequential.
Workflow Automation Services
Replace copy-paste between tools with reliable automated flows, using AI for the messy steps and plain logic for everything else.
LLM Integration Services
Add summarising, extraction, drafting or classification to the software you already run, built as an engineered feature with tests, limits and logs.
Ready to start your Multi-Agent System Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.