Multi-Agent Systems

This is for you if

• Your workflow consists of multiple stages and different tasks, and handling everything with a single agent makes the system more complex, less accurate or harder to control.
• You want parts of a process to run in parallel to reduce processing time, or you want the output of one stage to become the input of the next.
• You want each part of the process handled by a specialised agent: one researches, another produces content, another reviews the output, and the system delivers the final result.

When not to hire me for this

If your process is simple enough to be handled correctly by a single agent or a standard workflow, a Multi-Agent architecture will likely introduce unnecessary complexity. Multi-Agent does not mean adding more agents; it becomes useful when dividing a process between several actually improves accuracy, enables parallel execution or provides better control.

How it runs

1. 1

   Process analysis and task decomposition

   First the whole process is analysed to identify dependencies, independent tasks and the areas that need different expertise or logic. We then determine which parts should be handled by separate agents.

2. 2

   Designing the agent architecture

   Each agent's role and responsibilities are defined. Depending on the process, the system may include a central agent responsible for coordination, or the agents may operate as a defined sequence where the output of one becomes the input of the next until the final result is produced.

3. 3

   Building communication and information flow

   The flow of information between agents is designed. Some tasks can run in parallel while others follow a sequence. Context and outputs are passed between stages so each agent has the information required to do its job.

4. 4

   Connecting tools and data sources

   Agents are connected to the resources and systems they need: APIs, databases, CRMs, files, RAG, the web, Telegram, email and internal tools. Each agent is given access only to the tools and information its own role requires.

5. 5

   Output control, review and correction

   One agent's output can be reviewed by another. If an error or an inconsistency with the defined rules is detected, the output can be sent back to an earlier stage for correction. This structure is particularly useful in content production and other multi-stage processes where output quality has to be controlled.

6. 6

   Defining human intervention points

   For processes where a human decision or approval matters, human-in-the-loop is placed at defined points. There the system can present its output or recommendation and pause until the required approval is given.

7. 7

   Testing, deployment and optimisation

   Agents are tested individually and in coordination with one another. Error paths, information transfer, parallel execution, output quality and escalation points are all tested, and after deployment the architecture is optimised against real performance.

What you are left with

A Multi-Agent system that divides complex tasks between specialised agents, can run parts of a process sequentially or in parallel, review and refine outputs, and involve humans at defined points in the workflow.