Multi-Agent System Project
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Agent Roles and Responsibilities

2 · 5 min · 5/23/2026

Learn Agent Roles and Responsibilities in our free Multi-Agent System Project series. Step-by-step explanations, examples, and interview tips on Toolliyo Academy.

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Agent Roles and Responsibilities — Multi-Agent System Project
Advanced track — AI agents

Advanced Agent Roles and Responsibilities in Multi-Agent System Project. Deep dive with production-oriented examples—not a shallow overview.

Architecture & mental model

Agentic apps combine LLMs with tools (search, SQL, APIs). In .NET, Semantic Kernel plugins wrap functions the model can invoke. Reliability requires guardrails, logging, and human approval for destructive actions.

Implementation (production-style)

Type the code below; change names and types to match your domain. Compare with how AI agents teams structure layers in mature codebases.

// Semantic Kernel pattern (illustrative)
var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion(modelId, apiKey)
    .Build();

kernel.Plugins.AddFromType();

var result = await kernel.InvokePromptAsync(
    "Find order 1042 status and email summary to support@company.com",
    new KernelArguments { ["customerId"] = 1042 });

Decision checklist

  • Requirements: What are latency, consistency, and security needs for "Agent Roles and Responsibilities"?
  • Boundaries: Which layer owns this logic (UI, API, domain, infrastructure)?
  • Failure modes: What happens when dependencies time out or return partial data?
  • Observability: What logs or metrics prove this feature works in production?

Hands-on lab (45–60 min)

  1. Reproduce the primary example for "Agent Roles and Responsibilities" in a scratch project using AI agents.
  2. Add one automated test (unit or integration) that would fail if you break the core behavior.
  3. Introduce a deliberate bug (wrong lifetime, missing await, wrong dependency order) and observe the symptom.
  4. Document one trade-off you would present in a design review.

Pitfalls senior engineers avoid

  • Unbounded tool loops without max steps.
  • No cost/latency budgets.
  • Skipping evaluation on tool-selection accuracy.

Interview depth

Question: Explain Agent Roles and Responsibilities to a junior developer in 2 minutes, then list two trade-offs.

Strong answer: Start with the problem it solves, describe one real project usage, mention a failure you debugged or would test for, and close with alternatives (when not to use this approach).

Next level

Pair this lesson with official docs for AI agents, then read source or decompile one framework call path involved in "Agent Roles and Responsibilities". Advanced mastery comes from combining reading, debugging, and shipping.

Summary

You completed an advanced treatment of Agent Roles and Responsibilities. Revisit after building a feature that uses it end-to-end; spaced repetition with real code beats re-reading alone.

Test your knowledge

Quizzes linked to this course—pass to earn certificates.

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Multi-Agent System Project

On this page

Architecture & mental model Implementation (production-style) Decision checklist Hands-on lab (45–60 min) Pitfalls senior engineers avoid Interview depth Summary
Design
Agent Roles and Responsibilities Message Bus vs Supervisor Pattern Tool Calling for Agents
Build
Implement Planner Agent Worker Agents and Handoffs Human-in-the-Loop Checkpoints Logging and Observability
Wrap-up
Cost and Latency Trade-offs Ethics and Guardrails Showcase on GitHub