Agentic Coding

What makes coding 'agentic'

Agentic coding is the shift from assistance to delegation. A traditional AI copilot is reactive: it watches your cursor and offers the next likely line, and a human stays in the loop for every edit. An agentic coding tool is goal-directed: you describe an outcome — 'add pagination to the results endpoint and update the tests' — and the agent decomposes it, reads the relevant files, makes edits across the codebase, runs the test suite, reads the failures, and corrects itself until the goal is met.

The defining mechanism is the loop. Where a copilot makes one suggestion, an agent runs a perceive–decide–act cycle: inspect the repository, choose an action, execute it through tools (editing a file, running a command), observe the result, and decide what to do next. That ability to act, observe, and retry is what lets it finish tasks rather than just start them.

This is also why agentic coding consumes far more compute than autocomplete — a single task is many model calls plus tool round-trips, which is the same dynamic behind why AI agents cost more than chatbots generally.

How agentic coding tools work in practice

Most agentic coding tools share an architecture: a language model for reasoning, a set of tools (read file, write file, run shell command, search the codebase), and a control loop that feeds tool results back into the model. Terminal-native agents like Claude Code run directly in your shell against the whole repository; IDE-based tools like Cursor embed the agent in the editor; and fully delegated agents like Devin spin up their own environment to take a ticket from description to pull request.

Open-source options such as OpenHands offer the same agentic loop without subscription pricing — you self-host and pay only model API costs. Across the board, the trade-off is supervision: more autonomy means faster delegation but a greater need to review the diff, because an agent that edits many files can also introduce subtle errors across them.

Practical agentic coding usually keeps humans at the checkpoints that matter — reviewing the plan before a large change, and reviewing the diff and test results before merge — while letting the agent own the mechanical middle.

What is agentic coding?

Agentic coding is software development where an AI agent autonomously completes multi-step engineering tasks — reading the codebase, editing files, running tests, and fixing its own errors in a loop — instead of only suggesting the next line. You delegate a goal and review the result, rather than driving every keystroke.

How is agentic coding different from GitHub Copilot?

Classic Copilot autocomplete is reactive: it suggests the next line as you type and you accept or reject it. Agentic coding is goal-directed: the agent plans, edits across files, runs tests, and iterates on a whole task with minimal supervision. Copilot now also offers an agent mode that works this way.

What are the best agentic coding tools?

Leading agentic coding tools in 2026 include Claude Code for terminal-native autonomous development, Cursor for agentic editing inside an IDE, and Devin for fully delegated tasks. OpenHands is the strongest open-source option, offering the same agentic loop with no subscription — you self-host and pay only model API costs.

Is agentic coding safe to use on production code?

It can be, with review checkpoints. Because an agent edits many files and runs commands autonomously, teams keep humans reviewing the plan for large changes and the diff plus test results before merge. Run agents in version control with tests so any mistake is caught and reversible.