You’ve got a 500k-line monorepo, a Q1 mandate to “adopt AI coding,” and three tools that all claim to be the answer while behaving nothing alike. Cursor 3 wants to live in your editor. Amp wants your terminal and bills you in raw tokens. Factory’s Droid 2026 wants to own the whole SDLC and file the PR itself. Meanwhile your team burned $4,300 last month on requests nobody can attribute, one engineer’s agent silently rewrote a migration at 2am, and your security lead is asking whether any of this touches customer data. The demos all work. Your codebase is not the demo.
This guide is for working developers, tech leads, and platform engineers evaluating AI coding tools for real teams — you already know git, CI, and how your build works, and you don’t need an explanation of what an LLM is. It assumes you can read a pricing page and a SOC 2 summary. It is not a beginner’s “how to prompt” tutorial, not a Copilot autocomplete review, and it deliberately skips the toy to-do-app benchmarks that make every tool look identical.
Honest framing: these agents are genuinely strong at mechanical breadth — renaming across hundreds of files, porting test suites, tracing a symbol through layers nobody has touched since 2022. They are weakest exactly where it hurts most: silent semantic drift in business logic, confidently wrong assumptions about your undocumented invariants, and migrations that compile clean and behave differently. Human review is non-negotiable on anything touching auth, money, data migrations, and concurrency — and this guide tells you which tool’s review surface actually helps you catch that, and which one just gives you more diff to skim.
What This Guide Covers
- A clear-eyed map of how the three tools diverged architecturally — so you understand why they behave differently instead of guessing from feature lists
- The core mental models — context handling, agent loops, and harness design — that let you predict a tool’s failure modes before you hit them
- A one-hour setup path for all three side by side, so your evaluation starts from a fair baseline instead of whichever one you configured first
- A large-monorepo refactor benchmark with the results laid out plainly — including where each tool lost the thread
- A legacy migration and spec-driven workload comparison, the scenario most enterprise teams are actually buying for
- PR review throughput measured across Bugbot, Oracle, and Droid Reviews — signal, noise, and what each one reliably misses
- The terminal-native versus IDE-native tradeoff, and how background agents, parallel runs, and git worktrees change your team’s throughput ceiling
- A cost breakdown of requests versus tokens versus seats, including the pricing traps that only surface in month three
- Security and compliance posture compared head to head — SOC 2, zero-retention, self-hosting, and bring-your-own-key — in terms your security review will accept
- Extensibility in practice: MCP servers, custom tools, rules files, and wiring agents into CI without creating a rubber-stamp pipeline
- Documented failure modes for each tool — the specific breakages vendors don’t demo, and the guardrails that contain them
- A weighted scoring rubric you can adapt, with concrete recommendations by team size from solo to enterprise
- A migration path off Copilot that doesn’t strand your existing config, rules, and team habits
- A structured 30-day pilot protocol with measurable success criteria, so your decision rests on your data rather than a vendor’s
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