What is Agentic Coding?

Agentic coding is a way of building software in which AI agents write the code (entire modules, tests and infrastructure configuration) while developers define goals, set constraints, orchestrate the agents and review the results. The developer's core skill shifts from typing code to directing the process: less syntax, more architecture.

How it works

The working loop is short: the developer describes the task precisely: goal, constraints, context. The agent writes a first version, runs it, fixes what breaks. The developer reviews, corrects course, lets the agent iterate. What used to be a day of implementation becomes an hour of orchestration and review. In larger setups, several agents work on different parts of a codebase in parallel (multi-agent orchestration), and the developer becomes a conductor rather than a solo instrumentalist.

Not the same as autocomplete

Tools like the classic GitHub Copilot are driver-assist systems: they suggest the next line, the developer still writes the program. Coding agents navigate the codebase on their own. That paradigm difference, inline assistance versus autonomous task execution, matters more than any individual tool. We compare the current tools in detail in Copilot vs. Claude Code vs. Cursor.

What it changes for a team

Three things, none of them optional. Review becomes the bottleneck: when an agent produces 500 lines in five minutes, a review process built for hand-written code jams, and it has to adapt. Precision becomes the skill: the developers who benefit most are those who can specify exactly what they need. Measurement becomes the referee: whether AI actually makes the team faster is answered by throughput and cycle time, not by impressions. In our engagements we measure both; at Novadex, restructuring the dev organization brought cycle time down to 10 days.

The craft: five practices that outlive the tool churn

New agent tools ship monthly; the methodology underneath is remarkably stable. Across camps, including the published working habits of the toolmakers themselves, the same five practices carry the results:

1. Close the verification loop. Give the agent tests and a way to actually run the product. An agent that can check its own work iterates without you: the single highest-ROI habit in agentic coding.
2. Plan before large changes. A well-scoped plan lets an agent finish many tasks in one pass.
3. Turn mistakes into memory. Every repeated error becomes a rule in a persistent file (CLAUDE.md, rules): knowledge that compounds.
4. Parallelize. Several agents on git worktrees are a capacity, not a convenience.
5. Keep contexts small. Delegate research and review to sub-agents instead of dumping everything into one thread.

How to weight these depends on team, codebase and domain: a three-person team on a grown monolith works differently than a twenty-person product team. That tuning is precisely what we do with teams in engagements; the toolmakers' own methods are compared in Copilot vs. Claude Code vs. Cursor.

Why it matters now

This is not a future bet. Agents write production code today, this website included. The teams that adopt the working model early gain a structural advantage in output and cycle time; the developers who refuse to learn orchestration are, as we put it in our essay on the end of hand-written code, the COBOL programmers of 2030. How we bring teams there is described in AI Applications and Dev Org Modernization: hands-on, in 100-day cycles, with the knowledge staying in your team.

Common questions

Is agentic coding the same as AI-assisted development?

No. AI-assisted development usually means autocomplete and chat: the developer still writes the code, just faster. In agentic coding, the agent writes the code (modules, tests, configuration) and the developer defines goals, sets constraints and reviews results.

Which tools are used for agentic coding?

The current toolbox includes Claude Code (a terminal-based coding agent), Cursor (an AI-first IDE with agent mode), and multi-agent setups where several agents work on different parts of a codebase in parallel. The tools change quickly; the working model (agents write, humans orchestrate) is the stable part.

Does agentic coding work in legacy codebases?

Yes, and that is where it earns its keep. Agents navigate and refactor existing code well, provided the team gives them context and reviews rigorously. The constraint is rarely the codebase; it is the team's review process and measurement discipline.