Both agentic AI and Copilot tools are changing how .NET developers work, but in different ways and for different jobs. This article breaks down what each one actually does, how they fit into typical .NET workflows, and how to use them together without losing control of your codebase.
What Is Agentic AI?
An AI agent is software that can reason about a goal, decide which tools or APIs to call, and carry out multi-step tasks without needing a human to direct each step. You give it an objective; it figures out how to get there.
Microsoft describes agents as "autonomous software components" that set their own sub-goals, use tools, and can even collaborate with other agents. In .NET terms, you might build one using Semantic Kernel's Agent Framework, Azure OpenAI with function calling, or Microsoft's AutoGen. A typical use case: the agent receives a natural-language requirement, queries a documentation store, calls a REST API, and writes a structured response back to your system, all in one run.
What Is a Copilot?
Copilot tools (GitHub Copilot, Visual Studio IntelliCode) sit inside your IDE and respond to what you are doing. They autocomplete methods, generate unit tests, explain code, fix syntax, and suggest refactors, but only when you ask or when their inline completion kicks in. Every suggestion requires a developer to accept or reject it. Nothing happens autonomously.
GitHub also offers a Copilot Coding Agent (cloud-based), which you can assign to a GitHub Issue by mentioning @copilot. It will open a pull request with the requested change. That PR still requires human review before merging, so the developer stays in control throughout.
The core difference: agents drive workflows forward on their own; Copilot tools wait for you and respond to you.
How They Fit Into .NET Workflows
Copilot in the IDE
Install GitHub Copilot or Copilot Chat in Visual Studio or VS Code. From that point it is part of your daily coding, suggesting completions as you type. You can define project conventions via a .github/copilot-instructions.md file so that suggestions match your team's code style. The .NET MAUI team uses this approach to keep AI suggestions consistent with their project structure.
Copilot Cloud Agent
For more repetitive work, assign GitHub Issues directly to the Copilot agent. It analyses your solution and opens a PR with the changes. For example, you can ask it to identify missing unit tests in an ASP.NET project and generate them as a separate PR. By default, Copilot agent pushes to a copilot/ branch and requires sign-off before anything merges, which keeps your main branch protected.
Custom Agents in .NET
For bespoke workflows, Semantic Kernel gives you a .NET SDK with an Agent Framework. You define skills (functions the agent can call), register them on a Kernel backed by an AzureOpenAIClient or OpenAI Chat API, and let the LLM decide which tools to invoke based on the current goal.
Below is a simplified example using Azure.AI.OpenAI to build a single-turn tool-calling agent:
using Azure.AI.OpenAI;
var client = new AzureOpenAIClient(endpoint, new AzureKeyCredential(apiKey));
var chat = client.GetChatCompletionsClient(deploymentName);
var searchFunction = new ChatFunction(
"SearchKnowledgeBase",
"Retrieve relevant info from knowledge base",
new() {
new FunctionParameter("query", "text", "The user's question")
});
var messages = new List<ChatMessage> {
ChatMessage.CreateSystemMessage("You are an AI assistant. Use the function to find data."),
ChatMessage.CreateUserMessage("What is the latest price for product X?")
};
var options = new ChatCompletionsOptions {
Messages = { messages[0], messages[1] },
Functions = { searchFunction }
};
var response = await chat.GetChatCompletionsAsync(options);
var choice = response.Value.Choices[0];
if (choice.FunctionCall != null)
{
var fnName = choice.FunctionCall.Name;
var fnArgs = choice.FunctionCall.Arguments;
var query = JsonDocument.Parse(fnArgs)
.RootElement.GetProperty("query").GetString();
string resultText = SearchKnowledgeBase(query);
messages.Add(ChatMessage.CreateFunctionMessage(fnName, resultText));
var finalResponse = await chat.GetChatCompletionsAsync(
new ChatCompletionsOptions {
Messages = { messages[0], messages[1], messages[2] }
});
Console.WriteLine(finalResponse.Value.Choices[0].Message.Content);
}
In production, SearchKnowledgeBase would call Azure Cognitive Search or a SQL database. Semantic Kernel wraps this kind of pattern in higher-level abstractions and supports multiple tools, memory, and multi-agent orchestration.
Deployment and Architecture
Copilot in the IDE needs no infrastructure changes, just install the extension and configure GitHub access. The Copilot agent runs on GitHub's infrastructure; you enable it in your repo settings.
Custom agents are a different story. They typically run as Azure Functions or ASP.NET Web APIs. Store API keys in Azure Key Vault or .NET user secrets, never hardcoded. Connect agents to your data sources (Cognitive Search, SQL, Cosmos DB) within private network boundaries where possible. For Azure OpenAI, use private endpoints to keep traffic off the public internet.
Observability
For custom agents, log everything: prompts, tool calls, responses, token counts, and latency. Application Insights and Azure Monitor work well here. Set up alerts on error rates and cost metrics since LLM token usage can grow quickly if something is misbehaving.
For Copilot, GitHub Enterprise provides usage dashboards showing completions per user and acceptance rates. Track merge rates for Copilot agent PRs: Microsoft's own .NET runtime data showed agent PRs merged at around 68% success versus 87% for human PRs, which underlines why review steps matter.
Security
Agents carry more risk than inline Copilot because they can take actions, not just suggest them. A few things to get right: restrict which external endpoints agents can reach (GitHub's Copilot agent includes a built-in firewall for this; custom agents need equivalent controls); do not send PII or proprietary code to third-party LLMs unless you are covered by a data processing agreement; use retrieval-augmented generation to ground agents in real data rather than relying on model memory; and run static analysis and security scanners on all AI-generated code before merging. GitHub's Copilot Code Review can flag vulnerabilities including secret scanning and CodeQL issues.
For Copilot on GitHub, the enterprise licence includes a privacy guarantee that your code is not used for model training. Check your licence tier before pointing Copilot at proprietary repos.
Team Practices
Treat AI-generated code the same as any other contribution. It goes through your normal review process, passes CI, and gets a second pair of eyes. The merge rate data mentioned above is a clear reason not to skip this.
Store reusable prompts as .md files in your repo. GitHub supports prompt flow files, and Semantic Kernel treats prompts as first-class assets. Keeping prompts version-controlled means the team can refine and share them over time.
Configure GitHub branch rules so that Copilot agent PRs cannot bypass CI or merge without at least one human approval. Only repo writers should be able to trigger the agent. Start with one team and one project, gather concrete feedback, and do not try to automate critical production processes before you have confidence in the output quality.
Comparison
| Copilot (inline / agent) | Custom Agentic AI | Hybrid |
|---|
| Capability | Code suggestions, boilerplate, small task automation | Multi-step orchestration, tool use, cross-system workflows | Both, applied where each fits best |
| Control | Fully developer directed | Requires explicit guardrails | Copilot interactive; agents gated behind CI and review |
| Setup effort | Low (IDE extension or GitHub settings) | Higher (agent code, API integration, monitoring) | Moderate; grow complexity incrementally |
| Latency | Near-instant for completions; minutes for agent PRs | Multiple network round-trips; not real-time | Use Copilot for immediate needs, agents for async tasks |
| Cost | Per-seat subscription | LLM token usage per task; hosting overhead | Pay-as-you-go for agents; subscription for Copilot |
| Risk | Low; human review at every step | Higher; larger blast radius if something goes wrong | Mitigated through phased adoption and review gates |
For routine coding (writing methods, generating docs, refactoring a class), Copilot is the right tool. It is immediate and keeps you in control. For larger automated tasks (batch test generation, ETL pipelines, codebase-wide analysis), an agent can act on a high-level goal and handle the steps independently.
Adoption Roadmap for SME .NET Teams
Step 1: Pilot Copilot on one team. Set up .github/copilot-instructions.md to encode your project conventions. Run it for a sprint and measure acceptance rates and perceived productivity.
Step 2: Define review rules. Create a short checklist for AI-generated code. Label AI PRs. Require a human approval and green CI before anything merges. Set up branch protections.
Step 3: Build a simple agent. Pick a low-risk, non-customer-facing task: populating test data, summarising internal documents, or generating stub implementations. Use Semantic Kernel or Azure Functions with Azure OpenAI. Keep scope narrow.
Step 4: Add observability. Log all agent interactions (prompt in, tool calls, response out, cost). Start measuring Copilot usage through GitHub's admin dashboard. Build a baseline before scaling.
Step 5: Expand carefully. Once the pilot succeeds, move to higher-value workflows: CI/CD automation, support ticket triage, or data extraction pipelines. Combine Copilot and agents where it makes sense: Copilot handles individual task completion; agents handle the orchestration layer.
Step 6: Treat prompts as code. Maintain a shared prompt library. Include prompt changes in pull requests. Review and iterate them the same way you would any other logic in the codebase.
Step 7: Review regularly. Audit AI usage, output quality, and cost quarterly. Adjust model choice and prompt design based on what the data shows.
Conclusion
Copilot and agentic AI are not competing approaches. Copilot makes individual developers faster; agents automate workflows that would otherwise require multiple manual steps. On a .NET team, the practical combination is Copilot for daily development and Semantic Kernel or Azure OpenAI agents for orchestrated tasks, with clear review gates, observability, and security controls applied to both.
Start small, measure everything, and expand only once you have confidence in the outputs. The tooling is mature enough to be genuinely useful; the risk is in over-automating before your review processes are ready for it.