Glossary
What is
context engineering?
Context engineering is the practice of shaping the information and action space an AI agent uses during live execution, not just during prompt drafting. It includes prompts, workspace files, tool definitions, runtime state, permission boundaries, prior steps, and operational constraints that influence decision quality. In production workflows, these signals determine whether an agent selects the right tool at the right time, asks for missing data, or continues reasoning in text. Strong context engineering reduces avoidable tool calls, lowers failure loops, and makes behavior more reproducible across runs. Without it, even a capable model can make poor execution choices because critical state is missing, ambiguous, or presented too late. This is why high-performing agent teams treat context design as core infrastructure rather than copywriting. It functions as execution architecture, not just communication style, especially in multimodal workflows with strict reliability goals.
Why it matters
A capable agent needs more than a strong model. It needs the right context to understand what tools exist, which files are authoritative, what actions are allowed, and what success should look like for the current step. If those signals are weak, the agent may skip the right tool call, select the wrong capability, or trigger actions too early. Good context engineering improves reliability because it aligns model reasoning with execution reality.
For AnyCap, context engineering directly affects capability invocation quality. It determines when an agent should call image generation, video generation, image read, or video analysis through the runtime, and when it should remain in text. Better context means fewer blind calls, cleaner multimodal routing, and more predictable outcomes for teams operating across agent environments.