Forget LeetCode. After 8 months and one $340k Black Friday incident, here's a rigorous, task-by-task comparison of Cursor, Devin 2.0, GitHub Copilot Workspace, and Sourcegraph Cody on real production workloads — with a scoring rubric, failure modes, and the exact safeguards that would have saved us.

In late 2024, we had a 6-hour incident that cost roughly $340k in SLA credits. Root cause: a Cursor-generated database migration that looked clean in review, passed CI, and silently dropped a partial index on a 2.1 billion row Postgres 15 table. Query latency spiked from 4ms to 8 seconds. On Black Friday weekend.
That incident forced me to stop treating AI coding agents as productivity multipliers and start treating them like extremely confident junior engineers who occasionally hallucinate foreign key constraints. What followed was eight months of structured testing across three production services. Here's what I actually found.
I tested four agents — Cursor 0.42, GitHub Copilot Workspace (agent mode), Devin 2.0, and Sourcegraph Cody — against four production tasks of increasing complexity:
ZADD NX with sliding window semanticskafka-go and linkedin/goavropprof heap profiles.proto generation and interceptor middlewareEach agent was scored 1–5 across four dimensions: context accuracy (did it understand the existing code?), correctness (did the output run without modification?), failure transparency (did it flag what it didn't know?), and review survivability (did a senior engineer approve it unchanged?).
| Agent | Context Accuracy | Correctness | Failure Transparency | Review Survivability |
|---|---|---|---|---|
| Cursor 0.42 | 5 | 3 | 2 | 3 |
| Devin 2.0 | 3 | 5 | 4 | 4 |
| Copilot Workspace | 3 | 3 | 2 | 2 |
| Sourcegraph Cody | 4 | 3 | 4 | 3 |
Cursor traced a bug through four service boundaries in under 90 seconds using its codebase-wide embedding index. Impressive. But it suggested GETDEL for cache invalidation without noting that command requires Redis 6.2 — we were on 5.0. It also invented a method signature in our internal Go SDK that simply did not exist. The index was a partial one created with CREATE INDEX CONCURRENTLY idx_orders_user_active ON orders(user_id) WHERE status = 'active' — Cursor's migration dropped it without comment. Lesson: always run EXPLAIN ANALYZE before applying any schema change it generates, full stop.
Devin is the only agent that executes code in a real sandbox. During the Kafka consumer task, it caught a deserialization edge case in our Avro schema — a nullable union type that goavro handles differently depending on field ordering — because it actually ran the integration suite and read the stack trace. No other agent came close on correctness for stateful tasks. The tradeoff: 3x slower than writing it yourself on simple work, and the team tier is $500/month. At a 20-person startup, that's a CFO conversation.
Strong on stateless utility functions. The moment you introduce shared state, database transactions, or environment-specific topology — say, which Kafka topic maps to which consumer group per environment — it falls apart silently. It doesn't flag what it doesn't know, which is the most dangerous failure mode of all.
Cody earns its place for code explanation and documentation. Its graph-aware indexing traced every call site across 40 repos for a legacy auth function that nobody remembered writing. I didn't use it to generate production code; I used it to understand why cursed production code exists — and that alone justifies the seat license.
Speed of generation is the wrong benchmark. Measure review survivability and silent failure rate instead. Set up Datadog monitors with explicit anomaly alerts — or at minimum structured logging with log/slog in Go — before shipping anything an agent wrote. The Black Friday incident wasn't Cursor's fault. It was mine for not running EXPLAIN ANALYZE on a migration touching 2.1 billion rows. The agent gave me something plausible. I shipped it. Own your blast radius.