Guide
By AnyCap Team
Context engineering for agents
Context engineering is the practice of shaping what an AI agent can see, what it can trust, and what it should do next during a live task. It is not only prompt wording. The agent also depends on workspace state, tool definitions, capability availability, previous steps, permission boundaries, and runtime policies that control execution. Those inputs determine whether the agent should keep reasoning in text, ask for missing data, or call a concrete capability at the right moment. In multimodal workflows, this decision quality matters more than style. A strong model can still fail if context is noisy, incomplete, or contradictory. Good context engineering keeps decision signals explicit so the agent can move from intent to action through a stable runtime such as AnyCap. When teams formalize this layer, they usually see fewer retries, cleaner tool selection, and faster completion on complex cross-modal tasks.
Workspace context
The evidence an agent should carry into a tool decision
Workspace context is the operational part of context engineering. It is the state that can be checked before an agent spends money, changes a file, publishes an output, or repeats a task. Keeping these signals explicit makes the resulting decision auditable rather than merely fluent.
Repository evidence
Files, configuration, tests, and current diffs tell the agent what already exists. They should outrank a generic assumption copied from an earlier task.
Execution state
Running processes, command output, task IDs, generated artifacts, and failed requests determine whether the next action is retry, recovery, inspection, or a new call.
Tool contract
A tool name is not enough. The agent needs the current operation, mode, accepted fields, permission boundary, and output shape before it can prepare a valid call.
Decision history
User corrections and prior choices prevent repeated work. They also explain why a workflow selected one model, reference, or review gate over another.
Verifiable routing
Four checks before the agent moves from reasoning to action
These commands do not replace reasoning. They give the reasoning current facts: runtime health, available models, accepted inputs, and the point where a human should take control.
Is the runtime ready?
anycap statusStop and repair authentication or connectivity before composing a paid media request.
Which models exist now?
anycap image models
anycap video modelsUse catalog output as current state instead of choosing a remembered or retired model ID.
What does this mode accept?
anycap video models seedance-2.5 schema --operation generate --mode image-to-videoShape references and parameters from the live schema; do not infer fields from a different model.
Does a human need to decide?
anycap annotate ./candidate.png --no-wait
anycap annotate poll --session <session_id>Open a real review session and preserve its session ID. Pause when creative preference, risk, or irreversible cost cannot be resolved from workspace evidence alone.
Failure modes context engineering should prevent
Stale context: the prompt names a model or field that the current runtime no longer exposes.
Missing state: the agent starts a second paid job because it did not preserve the first task or artifact reference.
Over-broad context: unrelated files and old messages bury the constraint that should drive the next action.
Unclear authority: the agent guesses a subjective or risky decision instead of requesting an explicit human checkpoint.
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 only need a good prompt. It needs enough context to decide when visual inspection is necessary, when generation is required, and when text reasoning is sufficient. Without that decision context, the agent either over-calls tools and wastes budget, or stays in text too long and misses the action needed to complete the task. The quality of outputs depends on this routing step.
This is where AnyCap fits in practice. Instead of exposing many unrelated APIs with different credentials and response shapes, a capability runtime gives the agent one execution surface for image generation, video generation, image understanding, and video analysis. With a consistent runtime and clearer context signals, the agent can choose the right capability faster and produce workflows that are easier for teams to debug and repeat.
A simple decision 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