Definition
Coding agent
A coding agent is an AI agent that takes a tracked issue all the way to a reviewed pull request — reading the codebase, planning a change, writing and running tests, and opening a PR a human merges. It is distinct from inline code completion: it owns a multi-step task end to end, under review, rather than suggesting the next line.
Key takeaways
- A coding agent takes a tracked issue end to end — read, plan, edit, test, open a PR — rather than suggesting the next line.
- Its unit of output is a pull request, so the agent's work passes the same human review gate as any contributor.
- Tools like Linear Coding Sessions delegate an issue to Claude Code or Codex and attach the resulting branch and PR.
- In Planoda a coding agent runs under propose/approve governance with an audit trail, so delegating an issue stays reviewable.
The shift in 2026 is from autocomplete to delegation. A coding agent is handed a unit of work — a bug report, a small feature, a refactor — and runs a loop: it reads the relevant files, drafts a plan, edits code, runs the test suite, reacts to failures, and finally opens a pull request. Tools like Linear's Coding Sessions hand an issue to an agent such as Claude Code or Codex and stream the result back as a branch and PR attached to the issue.
What makes the pattern safe is that the unit of output is a pull request, not a direct push: the agent's work lands in the same review gate any human contributor passes through. A person reads the diff, runs it, and decides whether to merge — so the agent accelerates the boring middle of the task while a human keeps final say. Good coding-agent setups also scope tightly (one issue, one branch) and surface their reasoning, so a reviewer can follow what changed and why.
In Planoda, a coding agent is one species of governed agent: it picks up an assigned issue and proposes a pull request, while its consequential actions stay inside propose/approve governance and an audit trail — so handing it an issue is a reviewable, attributable decision rather than an unsupervised commit.
Related terms
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.
- Agentic WorkflowAn agentic workflow is a process in which one or more AI agents carry out a multi-step task with some autonomy — planning, calling tools, and acting on results in a loop — rather than a human driving each step. The agent decides the next action toward a goal, within boundaries its operator sets, turning AI from a single-response assistant into a worker.
- Propose / Approve (AI Governance)Propose/approve is a governance pattern for autonomous software: instead of executing a consequential action directly, an AI agent emits it as a proposal that a human or policy must approve before it runs. It keeps fast, read-only work autonomous while gating destructive or irreversible operations — the practical way to give agents real power without surrendering control.
- Tool CallingTool calling is the capability that lets a language model invoke external functions — query a database, search the web, create a record — by emitting a structured request the surrounding system executes. Instead of only producing text, the model can take actions and pull in live data, turning it from a text generator into the reasoning core of an agent.
- Agent skillAn agent skill is a reusable, named agent capability: a packaged workflow with a description, a system prompt, an allowed set of tools, and triggers that decide when it runs. Instead of re-explaining a task every time, you define the skill once and the agent applies it on demand — turning ad-hoc prompting into a library of repeatable, governed capabilities.