Standalone AI Agents
AI agents beyond chat interface.
Lesson
It’s amazing how skilled teams go to great lengths to create silos when context is missing, all with the best intentions.
The lesson in full: That lesson is not new by any means, but it is usually a much longer, political process. By this point in time, my ‘gatekeeper/pioneer’ role for AI work in Kognitwin (purely by the % of the code base I authored) has turned out to be a disadvantage, as multiple teams either assumed that ‘chat channel is THE way’ or preferred not to involve the engine’s author, and built their own solutions, fueled by the massive interest and mandate to work with AI. This is also on me, as ‘standalone AI agent’ was not documented as part of the original Reasoning Engine architecture. This led me to double down even more on the new way of documenting and using AI as an integral tool not just for our users, but for all internal teams as well.
What it is
While most interaction with AI in Kognitwin is via a chat interface, there are situations where ‘chat message in a channel with a user’ is not the ideal paradigm. This project created a ‘standalone execution environment’ for AI agents, with the Chat being one such execution environment. Includes a REST endpoint with SSE for streaming, task pipeline integration, SDK examples, and experimental MCP Server support.
Tools and technologies
MCP · REST · SSE · Task Pipeline · SDK
Lineage
Built on lessons from Reasoning Engine, Multi-Agent Reasoning Engine, Chat and Messaging Backbone, Task Orchestration V2.
Lessons carried into Agent-Operable Engines.