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How to choose an AI coding setup in 2026: tool classes, model families, benchmarks, subscription versus API and local runs, a pilot you can trust, and a price snapshot for 25 October.
AI does not advance evenly: in some fields it is already a professional tool, in others an impressive demo. A maturity scale, ratings across ten domains, and the key question — not "can it" but "how reliably and autonomously."
Make the specification the primary artifact: spec → plan → agent code → verify, 2026 tools, vs TDD/BDD, and where SDD breaks.
Agent PRs outpace review capacity: review debt, what AI can catch vs what humans must own, a post-diff checklist, human gates, and a team playbook for light vs deep review in 2026.
Evolution of IDEs and AI tool internals: LSP, repo indexing, context assembly, Tab/Chat/Agent modes, MCP, and comparison of Cursor, Copilot, JetBrains, CLI agents — what happens to code under the hood in 2026.
A practical model for agentic development: context, task decomposition, verification loops, multi-agent orchestration, security, DORA metrics, and the engineer's role in the 2026 SDLC.
A practical operating model for putting AI agents and RAG systems into production: policy-as-code, narrowly scoped tools, human approvals, adversarial testing, and audit evidence that engineers can actually use.
