AI agents that work together

Multi-Agent Systems

A multi-agent system is a group of specialised AI agents that work together on a task: one plans, others look up information, act on your systems or check the results. Meanify designs and builds multi-agent systems with guardrails, full logging and human approval at key steps, so they are reliable enough for real business processes.

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Who it's for

  • Companies with complex processes that span several systems
  • Teams that tried a single chatbot and reached its limits
  • Organisations that need AI assistants connected to internal data and tools
  • Product teams adding agent features to their software

Problems we solve

  • A general-purpose chatbot can't handle multi-step work reliably
  • No visibility into what the AI did or why
  • Risk of AI taking actions without control
  • Agents that work in a demo but fail in production

What's included

  • Agent architecture

    We choose the agents' roles, their tools and how they coordinate.

  • Tool and data integrations

    Agents connect to your systems and documents, including through the Model Context Protocol (MCP).

  • Guardrails

    Permissions, limits and checks on what each agent is allowed to do.

  • Human approvals

    Important actions wait for a person to approve them.

  • Evaluation and monitoring

    Test sets, logging and tracing to measure quality over time.

How it works

  1. Define the task

    We agree on the task, what success looks like and the risks.

    You approve the scope and limits.

  2. Prototype

    We build a first version and test it on a set of real cases.

    Results are reviewed case by case.

  3. Add controls

    We add guardrails, approval steps and monitoring.

    You decide which actions need approval.

  4. Roll out

    We roll out gradually and keep evaluating after every change.

    Quality is reviewed before each release.

Technologies we use

  • Anthropic Claude, OpenAI and open-source models
  • Model Context Protocol (MCP)
  • Agent orchestration frameworks
  • Retrieval-augmented generation (RAG)
  • Vector databases
  • Tracing and evaluation tools

Frequently asked questions

What is the difference between an AI agent and a multi-agent system?

An AI agent is a single model that can use tools to complete a task. A multi-agent system splits the work between several specialised agents that coordinate, which makes complex tasks easier to control, test and improve.

How do you keep AI agents under control?

Each agent only gets the permissions it needs, every action is logged, and important actions need human approval. We also test agents against a set of real cases before and after every change.

Can the agents use our internal data?

Yes. We connect agents to your documents, databases and tools with access controls, so they only see what they are allowed to see.

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