Systems | Development | Analytics | API | Testing

A metric says latency increased. A log says a request failed. A trace identifies the slow dependency. An APM agent points to the method. Manual instrumentation explains the business operation. Traffic capture shows the exact request and response that triggered it. Each layer answers a question the previous layer could not. Each also introduces a new cost, blind spot, and failure mode.

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You've mapped the architecture. You know your agents need to retrieve context from external tools, coordinate with other agents, and propagate mutations through your systems. The model logic is solid. What you haven't fully solved is what sits between those agents and everything they're trying to reach. That's the gap Kong was built to close. Multi-agent workflows live and die on context.

Auto-generated APIs changed how fast teams ship. Point DreamFactory at a database and you get a complete REST API in seconds: every table, full CRUD, live documentation, role-based security. For thousands of teams, that is the whole job. But auto-generated APIs mirror your schema. Your applications, and increasingly your AI agents, want something more deliberate: clean paths, shaped responses, and endpoints that match how the consumer thinks rather than how the database is laid out.