Multi-Agent Systems: When More Agents Help (and When They Don't)
Multi-Agent Systems: leave with a coordinator–worker implementation, a single-workflow baseline and an evaluation comparing both.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why multi-agent systems matters — the common problem: Multi-agent systems are built because they sound advanced, adding cost without better results.
- 2Core concepts in plain language: Coordinator–worker patterns, specialist agents, delegation, handoffs, shared state, parallel execution
- 3Going further: communication protocols, arbitration, evaluator–optimizer workflows, termination management, multi-agent evaluation
- 4Framework walkthrough: Workflow-vs-Agent Decision, Human-in-the-Loop Approval, Model Context Protocol
- 5Practical workflow, built live: A coordinator–worker implementation, a single-workflow baseline and an evaluation comparing both.
- 6How to measure it: Task success rate, Tool-call correctness, Human-approval rate, Cost per completed task
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A coordinator–worker implementation, a single-workflow baseline and an evaluation comparing both.
- A working understanding of Workflow-vs-Agent Decision and Human-in-the-Loop Approval
- A short list of measures to track: Task success rate, Tool-call correctness, Human-approval rate
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