GLOSSARY

What is agentic coding?

Agentic coding describes a model that runs its own loop: it reads the repository, decides what to change, runs something to check the change, and uses the result to decide again. It differs from completion and chat because the model controls sequencing, not just content. The environment supplies the feedback that keeps the loop honest.

The distinction is who chooses the next step. In agentic coding the model does, and the environment answers back.

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Agentic coding is development where a model directs its own steps, choosing files to read, commands to run, and edits to make, until a task is complete or blocked.

Three modes, three contracts

Autocomplete predicts the next tokens at your cursor. Chat takes a question and returns an answer you paste somewhere. Agentic coding takes a goal and runs a loop: read, edit, run, observe, repeat. The first two put you in the sequencing seat. The third puts the model there, with the repository as the source of truth. That shift is what makes a task, not a snippet, the unit of work.

What it is not

Agentic coding is not a synonym for good code, and it is not the same as a fixed workflow. A workflow follows steps someone wrote in advance. An agentic system decides the steps, which is why it handles tasks whose shape you could not predict. It is also why the failure modes differ: a workflow fails where it was designed to, an agent can fail anywhere.

What good looks like

Good agentic coding looks unremarkable from the outside. The diff is small and on point. The tests that ran are the tests that matter. The commit message explains the change, not the process. When the agent could not finish, it stops with a specific blocker instead of a vague apology. Autonomy should reduce the reading you do, not the confidence you have.

How to evaluate it

Give the agent a task that fails today and ask it to make it pass. Then look at what it did when the first attempt failed: did it read the error, or try the same thing again? Check whether the final claim is backed by a command that ran. And compare diff size to task size. A large diff for a small task is a warning.

How Hyperlane implements it

Hyperlane implements agentic coding as a verified loop. The agent plans, executes tool calls in parallel where they are independent, verifies with real commands, repairs failures, and delivers a pull request. The run happens in an isolated machine with a desktop, so browser work and shell work are one run. The model you pick sets a quality floor for every step.

What Hyperlane ships

  • Autonomous agent loop that plans, edits, runs, and verifies work end-to-end
  • Every run executes inside an isolated, resource-capped sandbox
  • Live execution traces - diffs, terminal output, and reasoning as it happens
  • GitHub-native workflow: pushes branches and opens pull requests itself
  • Web app, REST API, TypeScript SDK, CLI, and MCP server interfaces
  • Dollar-for-dollar credits - pay $20, get $20 of agent work

Frequently asked

Is agentic coding the same as vibe coding?

No. Vibe coding describes accepting generated code without close inspection. Agentic coding describes who sequences the work. An agentic system can be reviewed carefully, and a hand-written change can be accepted without reading it. The two terms answer different questions.

Does agentic coding replace developers?

It changes what a developer spends time on. The typing and the first pass move to the agent; judgment about what should exist, what the tests should prove, and whether the change fits the system stays with a person. Someone still owns the pull request.

What makes agentic coding reliable?

Feedback and limits. The agent needs a real environment to run commands in, a way to read the result, and a rule that stops it from claiming success without evidence. It also needs budgets, so a stuck loop ends instead of running all night.

Which tasks are a poor fit?

Tasks without a checkable outcome, tasks that need product decisions mid-flight, and changes that touch many unrelated systems at once. An agent can still attempt them, but you will spend the review time you saved on figuring out what it did.

How do I start with agentic coding?

Pick a small failing task with a clear success condition and a test you trust. Run the agent on a branch, read the diff, and check the evidence it produced. Start with the interface you already use: a workspace, a CLI, or an API.

Last verified 11 Nov 2026.

Sources: Anthropic - Building effective agents Hyperlane docs

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