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Glossary Term

Agentic Coding

By The Codegen Team · Updated March 26, 2026

A development approach where AI agents autonomously plan, write, test, and iterate on code with minimal human intervention.

Agentic coding represents a shift from AI as a suggestion engine to AI as an autonomous collaborator. Unlike traditional code completion tools that predict the next few tokens, agentic coding systems receive a high-level task description and independently break it into subtasks, write code across multiple files, run tests, interpret errors, and iterate until the task is complete.

The key differentiator is the feedback loop. An agentic system doesn’t just generate code and stop. It executes that code, observes the result, and makes corrections. This loop can run multiple times without human input, which means the agent can handle tasks that would require several rounds of copy-paste in a traditional LLM workflow.

Tools like Claude Code, Cursor, and Codegen operate in this paradigm, though they vary significantly in how much context they can access and how much autonomy they exercise.

In plain English

An AI that handles an entire coding task from start to finish — reading the problem, writing the code, running tests, and fixing errors — without you directing each step.

Why it matters

Most AI coding tools still need a developer present for every change. Agentic coding removes that requirement. The team describes the task, the agent executes it, and a pull request appears when it is done. That is the difference between AI as a typing accelerator and AI as a team member.

In practice

A developer writes a ticket: "Migrate the auth module from session tokens to JWT. Update all 14 services that call it. Test suite must pass." They assign it and move on. An hour later there is a pull request with a description, changed files, and passing CI. The developer reads the diff, suggests one change, and merges.

How Codegen uses Agentic Coding

Codegen handles the infrastructure that makes agentic coding work for a team rather than just one developer. The agent reads the ClickUp task before touching the codebase — requirements, specs, acceptance criteria, linked docs — so it understands why the change is needed, not just what files exist. It runs in a sandboxed environment, tracks cost per task, and reports progress back into the ticket. The part Codegen does not handle: tasks with vague or missing acceptance criteria still produce inconsistent output, same as any agent. Garbage in, garbage out applies here.

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