There is no definition of an "agentic framework" in your governance docs, assets, or current flows. The context here is Liminality, a question-decomposition and routing MCP server. It connects to Acme CRM and HubSpot. It does not define the general technical concept.
I will answer using general knowledge. This is a conceptual definition, not an operational query about your assets.
### What is an Agentic Framework?
An agentic framework is a software architecture or set of tools for AI models. It lets Large Language Models (LLMs) function as autonomous agents. Standard chatbots generate text in response to a prompt. An AI agent perceives its environment. It plans actions. It uses tools. It executes tasks to achieve a specific goal with minimal human intervention.
### How It Works for AI Agents
The workflow follows core components.
1. **Perception (Input/Context):** The agent receives a high-level goal. Example: "Migrate my Postgres database and notify the team." It gathers context from its environment. This includes user instructions, memory, or real-time data from connected tools like Acme CRM or HubSpot.
2. **Planning (Reasoning):** The LLM analyzes the goal. It breaks it down into sub-tasks. Frameworks like Liminality are relevant here. Liminality acts as a router. It decomposes complex questions into sub-questions. It grounds them to real tools. A broader agentic framework creates a plan or a chain of thought in this step.
3. **Tool Use (Action):** The agent selects and executes specific tools. * Use a Postgres tool to migrate the database. * Use a Gmail toolkit to send an email notification. * Use Asana to update a task status. The framework manages API calls. It handles input formatting and output parsing.
4. **Observation (Feedback Loop):** After execution, the agent observes the result. "Migration successful" or "Error: Connection timeout." It uses this feedback to decide the next step. If an error occurs, the agent may retry. It may adjust its plan. It may ask the user for clarification.
5. **Completion (Output):** Once sub-tasks are done, the agent synthesizes the results. It presents a final response to the user.
### Relevance to Your Project (Liminality)
Liminality aligns with the Planning and Routing phases. It reduces questions into sub-questions. It grounds them to real tools. This provides the orchestrator logic. It allows AI agents to use your connected toolkits. These include Airtable, Figma, and Gmail.
Let me know if you want to explore implementing an agentic framework with your existing assets. I can look at integrating Liminality with Motherboard or physea.ai.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai