Introduction to AI Agents using .NET

Advanced Introduction to AI Agents using .NET in AI Agents using .NET. 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 .NET 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 "Introduction to AI Agents using .NET"?
• 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 "Introduction to AI Agents using .NET" in a scratch project using .NET.
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 Introduction to AI Agents using .NET 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).

Summary

You completed an advanced treatment of Introduction to AI Agents using .NET. Revisit after building a feature that uses it end-to-end; spaced repetition with real code beats re-reading alone.