Most AI coding tools can generate code. Few can answer a more important question: what else needs to change?
In modern systems, a single API change can impact specifications, collections, backend services, frontend applications, tests, CI/CD pipelines, databases, documentation, and infrastructure. Experienced engineers know that implementing the change is often the easy part. Understanding its downstream impact—and updating every dependent system correctly—is where the real work begins.
This talk explores how autonomous agents can move beyond code generation by leveraging a Context Graph built from the artifacts engineering teams already maintain: API specifications, collections, tests, environments, source code, and runtime dependencies. Rather than operating on individual files in isolation, these agents can reason about relationships across an entire system.
Through real-world engineering workflows, including migrations, contract drift remediation, dependency analysis, large-scale refactoring, and test generation, we'll examine how context-aware agents can identify affected components, coordinate changes across repositories, validate outcomes, and produce reviewable pull requests.
You'll see why context is often the limiting factor in autonomous software development, and how APIs provide a durable foundation for building agents that understand the architecture, dependencies, and operational reality of modern software systems.