Define agentic AI in the SDLC

  • 5 minutes

Many developers already use AI in a familiar assistant pattern. An assistant responds to a prompt, generates output, and returns control to the user. An agent goes further: it can interpret a goal, decide on intermediate steps, use tools, and take action inside a workflow.

That difference matters because it changes AI from something that helps with development into something that participates in development.

In this unit, you'll learn

  • What makes an AI system agentic in a development context

  • How agent-based systems differ from assistant-based systems

  • How agent behavior appears inside GitHub workflows

Slide comparing GitHub Copilot as an assistant versus an agent. It contrasts suggestion-based help with autonomous multi-step actions like using tools and creating pull requests.

What makes an AI system agentic in a development context.

Assistant-based systems are typically reactive:

  • They depend on a user to decide what to do next.

  • They may suggest code, explain output, or summarize changes.

  • They don't independently move work forward inside a repository.

Agent-based systems are goal-driven:

  • They can interpret a task, develop an approach, and take steps toward completion.

  • They can use tools (for example, the GitHub API, CI workflows, or repository write operations) to produce durable outcomes such as branches, commits, and pull requests.

  • They can iterate based on feedback (checks, reviews, scans).

In GitHub, this model is often expressed through a pull-request-oriented workflow: the agent proposes changes on a branch, opens a pull request, and waits for review and validation before the change is merged.

Assistant versus agent?

It is behaving like an assistant when it:

  • Produces suggestions or explanations

  • Does not take repository actions

  • Requires the user to apply each step manually

An AI system is behaving like an agent when it can:

  • Maintain a goal across multiple steps

  • Decide intermediate actions

  • Use tools

  • Create or modify durable artifacts (branch/commits/PR)

  • Iterate based on feedback signals

How agent behavior appears in GitHub

In GitHub, agent behavior is visible through the same structures developers already use:

  • Branches and commits (what changed)

  • Pull requests (what is proposed, why, and for review)

  • Workflows and checks (what evidence exists)

  • Review comments and approvals (what humans accepted or rejected)

An agent does not replace the workflow. It enters the workflow as a participant.

Implementation examples

Agent behavior (PR-producing) A security alert is filed. The agent:

  1. Creates a branch (for example, agent/bump-dep-2026-04-03)
  2. Updates a dependency and lockfile
  3. Opens a pull request with a summary and plan
  4. Waits for CI checks and review feedback, then revises if needed

Assistant behavior (suggestion-only) You ask an assistant: "How do I safely update this dependency?" The assistant gives:

  • a set of recommended commands

  • a checklist of risks

  • suggested code changes You still create the branch and pull request yourself.

In the next unit, you'll examine the lifecycle that governs how agents plan, act, and evaluate.