Guide · By AnyCap Team
What is an agent harness?
An agent harness is the execution layer around an AI model that turns reasoning into real actions.
It provides access to files, terminals, tools, browser actions, permission boundaries, and runtime policies that control what is allowed during a task. The model may decide what should happen next, but the harness decides what can actually happen in production. This distinction is critical: planning quality depends on the model, while execution safety, observability, and repeatability depend on the harness. In practice, harness design is often the difference between a demo that works once and a workflow a team can run every day.
Agent model vs agent harness
| Layer | Role | Example |
|---|---|---|
| Model | Reasoning, planning, language generation | The LLM decides whether to read a file, ask a question, or call a capability |
| Harness | Execution surface and safety boundaries | Files, shell, browser, permissions, tool contracts, and workflow policies |
| Capability runtime | Curated capability layer the harness can expose | AnyCap provides image generation, video generation, image read, and video analysis through one interface |
What the harness controls in a real tool call
These commands expose three separate contracts. Authentication belongs to the execution environment, model discovery belongs to the capability runtime, and accepted inputs belong to the tool schema. The model can choose among those options, but it does not define or bypass them.
Verify access
anycap statusThe harness decides which authenticated identity and policy context may reach a capability.
Discover tools
anycap image modelsThe capability runtime returns the active catalog instead of asking the model to guess which tools exist.
Inspect the contract
anycap image models gpt-image-2 schema --operation generateThe schema defines the accepted inputs before the harness authorizes an execution request.
This verifies the current AnyCap CLI contract. It does not claim that AnyCap is a complete agent harness, or that every harness uses the same commands. AnyCap supplies capabilities that a harness can discover, constrain, and execute.
Where AnyCap fits
AnyCap is not the model itself and it is not the full harness. It sits inside the harness as a capability runtime that supplies the actions the model cannot perform on its own. That means the harness can expose multimodal operations through a stable contract instead of wiring a different provider integration for every new task. In practical terms, the agent keeps its familiar reasoning environment while gaining a consistent path to image generation, video generation, media understanding, retrieval, storage, and publishing when those actions are needed.
This distinction matters for architecture decisions. The harness is responsible for lifecycle control, permissions, tool routing, and execution policy. AnyCap is responsible for packaging capabilities so those policies can be applied consistently across multimodal actions. Together, they reduce integration drift: teams keep one CLI surface, one authentication flow, and one operational model across different agent shells. That makes debugging clearer, onboarding faster, and capability upgrades less expensive over time.
Apply this to one media task
Choose the workflow that matches your agent or input. Check setup, model access and cost before generation.
CLI version and updating · 0.6.5
Current release checked October 21, 2026: 0.6.5. Use the update command for your installation method, then verify the version and login in the same environment that runs your agent.
Direct binary installation
anycap updatenpm installation
npm install -g @anycap/cli@latestanycap --version
anycap statusVersion 0.6.5 retries transient downloads, recovers an expired video link by querying the same task, and saves multiple image outputs to numbered files. After a video download failure, resume the existing task instead of generating again.
anycap video tasks wait <task-id> -o result.mp4Dated examples retain the CLI version, output and charge from the original run. Updating the CLI does not revalidate an old result. Check the live model schema and current pricing before a new billed task.