Glossary
What is an
agentic workflow?
An agentic workflow is a goal-directed sequence of model decisions and real-world tool actions that runs to completion without requiring human approval at each step. The model decides what to do; the agent harness executes it; the results feed back into the next decision. This loop continues until the goal is met, a stop condition is reached, or the agent requests human input. What distinguishes an agentic workflow from a simple chat session is the combination of autonomy, tool use, and state persistence across steps. The model does not just generate text — it calls tools, reads file contents, generates images, searches the web, runs code, and stores results that subsequent steps build on. Agentic workflows are what make AI systems genuinely useful for multi-step production tasks rather than isolated queries. The reliability of each step depends heavily on which tools are available, how well they are described to the model, and what constraints govern execution. Workflows that cross capability boundaries — generating an image, then analyzing it, then publishing the result — require a runtime that covers all those actions through one consistent interface.
Why it matters
Agentic workflows are where capability gaps become most visible. A well-designed agent with strong reasoning but no access to image generation, vision, or web retrieval will fail at steps that require those actions — regardless of how capable its language understanding is. This means capability coverage is a workflow design question, not just a product selection question. The more diverse the workflow, the more types of capabilities it requires. Teams that plan workflow steps before selecting their capability layer avoid discovering gaps mid-execution, which is the most expensive time to find them.
For teams using AnyCap, the capability runtime is the tool layer that makes agentic workflows with generation, understanding, and retrieval possible. Without it, developers must build and maintain separate integrations for each action type, which adds failure surface and slows iteration. A runtime that covers image, video, vision, web search, web crawl, storage, and publishing in one install path reduces the integration overhead that would otherwise fragment the workflow into disconnected pieces. This is why teams running high-diversity agentic workflows typically benefit most from the runtime architecture.
EXECUTION PROOF
A concrete agentic loop
Suppose the goal is to collect current evidence about an AI release. The agent can decide that web retrieval is required, the harness can authorize the search capability, and AnyCap can execute the real call below. The returned result set becomes state for the next decision.
01 / Goal and state
Start with a result, not a script
The agent receives a goal and the current state. It does not begin with every future action fixed in advance.
02 / Decision
Choose the next useful action
The model decides whether it needs retrieval, generation, analysis, or human input. The harness limits that choice to allowed tools and policies.
03 / Execution
Call a capability through its contract
The runtime validates the request, executes the selected capability, and returns structured output rather than asking the model to simulate the action.
04 / Feedback
Use the result as the next state
The agent evaluates the returned evidence, then continues, stops, retries, or asks for review. That feedback loop is the agentic part.
anycap search --query "latest AI agent runtime releases" --time-range monthThis command proves the capability call, not the complete harness. Planning, permissions, memory, retries, and stop conditions remain the harness's job; AnyCap provides the callable capability layer.
Agentic workflow vs. chat and automation
| Pattern | How the next step is chosen | Practical boundary |
|---|---|---|
| Chat | One prompt produces one response | No durable multi-step execution loop |
| Fixed automation | Every step and branch is predefined | Reliable for known paths, but does not choose a new action from changing state |
| Agentic workflow | The model selects the next allowed action from the current state | Useful when the path cannot be fully specified before execution |