Skills: The Building Blocks of Agentic Intelligence

In the world of AI agents, there is a massive gap between "writing code that works" and "building a reliable system." Most agentic frameworks treat code execution as a disposable event. An agent writes a script, it runs, it completes, and the knowledge of how it worked vanishes.

To build production-grade automation, we have to treat Skills as first-class citizens.

The "In-Memory" Capture: Never Lose a Breakthrough

In-Memory Skill Tracking: From Execution to Published Skill

In our architecture, every time an agent executes code, the platform doesn't just run it—it captures it. By default, every successful execution is stored as an in-memory skill draft.

This is a fundamental shift in the agent's journey. Instead of the agent "guessing" how to do something again later in a long workflow creation process, it has a trail of proven, testable blocks. The agent can reference previous executions via unique IDs, effectively "learning" your environment as it builds.

The Secret Sauce: Output Parameter Mining

The biggest implementation gap in modern agent research (including Anthropic's latest work) is composability. You can't wire two functions together if you don't know exactly what the first one returns.

Standard code execution returns a string or a JSON blob, and the agent has to "hallucinate" the structure for the next step. We solved this with automatic output schema mining.

When a skill draft is captured, the platform analyzes the return values in the sandbox and generates a typed schema:

yaml
# Automatically mined from execution
outputs:
  ticket_key: string
  assignee_id: string
  status: string

Because these signatures are formalized, agents can wire outputs to inputs with 100% reliability. This is what allows an agent to build a complex workflow autonomously—it's not guessing; it's following a verified data contract.

The Skills Lifecycle: Draft to Production

Transforming a conversation into a production system follows a clean three-step path:

Draft (Auto-capture): Every code execution is saved in-memory. The agent uses these to "scaffold" the workflow.
Publish (Persistence): Once a logic sequence is proven, the agent (or user) promotes it to a Published Skill. This stores the code immutably and formalizes the input/output contract.
Schedule (Automation): Published skills can be scheduled via cron or triggered via webhooks. This is where "agentic work" becomes "autonomous service."

Agent-as-Architect, User-as-Foreman

This architecture flips the traditional automation model. In tools like Zapier or N8N, the user is the builder, dragging boxes and wiring lines.

In our model, the agent is building the workflow. The user is along for the ride, providing subject matter expertise and business justification. The agent iteratively builds skills, tests them in isolation, mines their signatures, and composes them into a final production-ready system.

By making skills persistent and first-class, we move past "chatting with AI" and into building with AI.

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