Multi-Agent Systems
Some tasks are genuinely made of several different jobs: research, then calculation, then drafting, then a check. Asking one agent to do all of it tends to weaken the weakest part.
Why It Matters
An agent that only researches can be tested thoroughly on research. An agent that only calculates can be checked against known figures. Keeping each agent narrow means each one can actually be verified, instead of trusting a single broad agent on faith.
Without coordination, several agents working on the same task can duplicate work or contradict each other. A supervisor agent manages the order of work, passes structured results between agents, and stops the chain the moment something does not add up.
Research, calculation and drafting split across agents to produce a quote, report or proposal.
Checking a document or case from several angles at once, such as compliance, cost and risk.
One agent gathering information while another structures and summarises it for a decision.
A task that naturally passes through more than one function, each with its own agent.
One agent produces a result, a second agent checks it independently before it reaches a person.
Splitting a large analysis into parts that run in parallel and are reassembled by a supervisor.
We break the complex task into distinct parts, each with a clear input and output.
Each agent is built and tested narrowly for its one part of the work.
The supervisor agent manages the order of work and the structured handoffs.
The full system is run against real cases end to end before it goes live.
A person reviews the output, and the weakest agent in the chain is improved first.
Result on every part of the task
Weak points, easy to find and fix
Contribution of each agent
Handoffs, not improvised ones
Than one agent doing it all
By a person before it lands
We define the parts of a task carefully first, so each agent has a job it can actually be tested on.
A coordinating agent is built into every system, not bolted on after the specialists are done.
A weak result at any stage halts the handoff and goes to a person instead of flowing forward.
Each agent's output is logged on its own, so a problem can be traced to its source quickly.
We choose the approach that fits each agent's job and your hosting rules, with no lock-in.
It is a setup where a task is split between several AI agents, each built for one part of the work, rather than one agent trying to do everything. A supervisor agent coordinates the handoffs between them and pulls the results together for a person to review.
A single agent trying to research, calculate and draft a document at once tends to lose accuracy in at least one part. Splitting the work lets each agent be built and tested narrowly for its own part, which is easier to get right and easier to fix when something goes wrong.
Through a defined handoff structure: one agent finishes its part and passes a structured result to the next, coordinated by a supervisor agent. This is designed and tested upfront, so the handoffs are consistent rather than improvised at run time.
The system is built to detect a failed or low-confidence step, stop the chain at that point, and route it to a person rather than let a bad result quietly flow into the next agent's work.
Yes. Each agent's part of the work is logged separately, so you can see which agent produced which piece of a result and trace a problem back to its source.
Tasks that genuinely have distinct parts requiring different skills, such as research plus calculation plus drafting, or checking a document from several angles at once. If a task is really one step, a single agent is simpler and is what we would recommend instead.
Related
The other services in this section, and related pages from TCB Infotech.
No commitment. A practical look at how to structure the build.
