Guides
By AnyCap Team
Context engineering
for agents
Context engineering is the practice of shaping what an AI agent sees and how it interprets its environment. It goes beyond prompt wording. The agent also depends on workspace state, tool definitions, capability availability, prior steps, and execution rules. That is what determines whether it stays in text or calls a capability through a runtime like AnyCap.
The three practical layers
What the agent can see
The system prompt, workspace files, prior messages, tool definitions, and execution constraints all shape the action space.
What the agent can do
Capabilities are only useful when they are exposed in a way the agent can discover and trust during execution.
When the agent should switch from text to action
Good context engineering helps the agent decide when reasoning is enough and when it should call image generation, video analysis, or another capability.
Why it matters for multimodal agents
A multimodal agent does not just need a good prompt. It needs enough context to know when to inspect an image, generate a mockup, read a video, or keep reasoning in text. If the context is weak, the agent may overuse tools, skip the right capability, or call the wrong model.
This is where AnyCap fits. Instead of giving the agent many unrelated APIs, a capability runtime exposes image generation, video generation, image understanding, and video analysis through one interface. That reduces the number of decisions the agent must make at execution time.
A simple decision pattern
# Agent reasoning pattern
Need text only? stay in prompt
Need a new image? anycap image generate
Need to inspect a screenshot? anycap image read
Need to review a recording? anycap video read