agentic coding

Agentic coding is an approach to software development where AI agents plan, write, run, and iteratively improve code using tools and feedback loops under human oversight.

Unlike autocomplete that assists line by line, agentic coding treats the model more like an autonomous problem solver that can decompose high-level goals, execute the code it writes, run tests and other commands, analyze failures, and retry until a specification is met. This loop of generate -> execute -> evaluate -> refine is core to the paradigm where human supervision is key.

In practice, systems implementing agentic coding define principles and guardrails for safety, observability, and accountability. They also orchestrate multi-step or multi-agent workflows, which increasingly run in the background, in CI, or on a schedule rather than in a live terminal session. These workflows integrate with existing tooling, such as version control, testing frameworks, issue trackers, and deployment environments, often through MCP servers that expose those systems to the agent as tools.

A person in overalls pointing at a four-piece puzzle map labeled IDE, Cloud, CLI, and PR/Repo, with a map info legend beside it and a Python logo.

Tutorial

AI Coding Agents Guide: A Map of the Four Workflow Types

AI coding agents come in four types: IDE, terminal, PR, and cloud. Learn how each workflow fits into modern Python development.

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For additional information on related topics, take a look at the following resources:

By Leodanis Pozo Ramos • Updated Aug. 15, 2026
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