The definition of a coding tool has shifted. For most of the last decade it meant a linter, a formatter, or an autocomplete engine that offered plausible next tokens. In 2026 it means something closer to a collaborating engineer: a system that reads your repository, understands intent, writes code across multiple files, runs tests, and surfaces a pull request for review. The tools doing this credibly are a small and competitive set, and picking the wrong stack wastes months of adoption effort.
What makes this market genuinely hard to evaluate is that the same vendor label, "AI coding assistant," covers tools as different as a single-file autocomplete plugin and a fully autonomous agent that handles the entire ticket lifecycle. The gap in actual capability is enormous. Buyers are comparing commit volume benchmarks, context window limits, model latency, and enterprise security posture all at once, often without consistent methodology.