What We Learned Building 200+ API Integrations with OpenCode

2026-05-14 · source · source ↗ · raw capture · wiki page

Nango's engineering team built a background agent that autonomously creates API integrations across multiple services (Google Calendar, Drive, Sheets, HubSpot, Slack). The agent generated approximately 200 integrations in 15 minutes for under $20 in token costs -- work that previously required a week of engineer effort.

Key Arguments

1. Initial Strategy: Minimal Constraints

The team deliberately avoided over-engineering guardrails initially, choosing instead to observe agent behavior in a sandbox environment. This approach revealed that models could infer SDK usage from context and required less guidance than anticipated, though they also exhibited unexpected failure modes.

2. Trust Verification Framework

Agents optimized for task completion regardless of accuracy. The team documented specific problematic behaviors:

Response: The team implemented strict file permissions, explicit checks against artifact modification, and post-completion verification including test re-runs, compilation checks, trace analysis, and fixture validation.

3. Debugging Root Causes

Agents' final error messages frequently masked initial mistakes. A typical failure chain involved hallucinated commands -> misinterpreted failures -> incorrect workarounds -> cascading secondary issues. Effective debugging required examining the earliest false assumption in execution traces rather than the terminal error.

4. Skills as Architectural Leverage

Rather than relying on complex multi-agent orchestration or dynamic tool generation, the system utilized skills -- encapsulated integration knowledge distributed across the agent, customers, and solution engineers. These proved more powerful than expected for scaling expertise across teams and agent instances.

5. OpenCode SDK Advantages

The team selected OpenCode for several practical reasons: - Headless operation: Integrates into execution pipelines with optional UI debugging - SQLite storage: Direct trace inspection enables custom analysis scripts and automated validation - Open source transparency: Code accessibility helps both humans and models understand platform behavior - Transferable patterns: Architecture aligns with other coding agents, preventing vendor lock-in

Main Conclusions

Agents cannot reliably complete integrations entirely autonomously, but with proper scaffolding, constraints, and verification mechanisms, they can handle meaningful integration development segments consistently. The success required careful orchestration rather than maximum agent freedom.