Ask any engineer building an AI agent how long it took to ship their first integration and you'll get a number measured in weeks. Ask how long for the tenth and you'll hear something worse, because the work doesn't compound. Each new provider means a new auth flow, new rate limits, new data model, new failure modes. Nango's own customers were spending 30-40% of their engineering cycles on integration work before switching to the platform. Scalable access to reliable APIs will drive some of the greatest capabilities of production agents. AI workloads of the future require AI infrastructure and an open, scalable integration platform. Nango is building just that, and we’re excited to lead the Seed round and back founders Robin and Bastien.
Why Are Integrations the Bottleneck for AI Agents?
Integrations used to be a settings page. For AI agents, they're the entire product. The companies in our portfolio today are building agents that make hundreds of API calls per session across dozens of providers, where every call needs to be deterministic, idempotent, and fault-tolerant. If your agent can't reliably read from a CRM, write to a ticketing system, and sync with a knowledge base, you don't have an agent. You have a demo.
Having worked at MuleSoft, I saw firsthand how integration infrastructure became a platform category. MuleSoft, Twilio, Stripe, Plaid each proved that if you owned a connective layer, everything else got built on top of you. But all of those were designed for a caller who initiates one request and waits for a response. Agents work differently. They fan out across six APIs in parallel, each with its own rate limits and backpressure. They retry failed calls and need idempotency guarantees to avoid creating duplicate records. They need error responses with enough structure to reason about what went wrong, not a generic 500 that a human would triage manually. The volume, the concurrency, and the requirement for programmatic control are just different in kind from what the last generation of integration tooling was designed to handle.
Why Don't Unified APIs Solve This?
The unified API approach tried to solve this with abstraction: one endpoint per category, config-driven, provider-specific rough edges hidden behind a managed layer. That trade-off made sense when a product manager was toggling integrations from a dashboard. It stops making sense when a coding agent needs to write custom transformation logic, control retry behavior per provider, or handle OAuth token rotation across 50 providers that each implement refresh windows, scope formats, and tenant ID resolution differently. Config can't express that level of specificity. You need code.
MCP doesn't solve this either. Every MCP tool definition developers add, from name, schema, parameters, to description, burns the context window before the agent has done anything. Connect a dozen tools and the agent's reasoning capacity is already degraded. On top of that, MCP tools are frozen at the version when the developer published, when an API changes or an edge case appears, the agent is at the mercy of external MCP tool maintainers.
How Nango Approaches Integration Infrastructure
Nango is an open-source platform built on that conviction. Developers write real integration logic with full access to each provider's API surface across 700+ APIs and growing! Each integration starts from pre-built templates and layer custom logic on top: syncs, tool calls, webhooks, MCPs, batch operations. What makes this more than a preference is what it means in practice. The integration code is testable. It goes through PR review. It deploys through CI/CD. When something breaks in production, you can read the git blame instead of opening a support ticket with your integration vendor and hoping they reproduce it.
The auth layer alone solves a problem most teams underestimate until they're deep in it. Managing token rotation and credential storage across 50+ providers, each with their own quirks around refresh windows, scope formats, and tenant handling, is a full engineering workstream that Nango reduces to a single integration point. After deployment, integrations run on auto-scaled infrastructure with per-connection observability, structured logging, and alerting that tells you which connection, which provider, and which API call failed. The roadmap extends toward autonomous remediation, where agents detect and resolve integration failures without human intervention.
The Team and Traction Behind Nango
Nango processes billions of API requests per month for teams including Replit, Mercor, and Ramp. The open-source project has a 3,000+ member developer community that grew without incentive programs. Robin and Bastien are second-time founders running a lean, high-retention team that ships at a pace that consistently surprises us.
We think the company that owns the integration layer for AI agents will matter the way Stripe mattered for payments and Twilio mattered for communications. That category is taking shape now, faster than most of the industry expects. Nango is already running in production at the companies defining it.
If you're building AI agents and spending too much of your engineering time on integrations, try Nango. The project is open-source and the team is always looking to talk to developers working on hard integration problems.