The Thesis: The End of "Chat"
The era of the "Chatbot" is ending. The single-turn, question-answer paradigm is insufficient for complex industrial tasks. The future belongs to Agentic Systems—autonomous loops capable of planning, tool use, and self-correction.
The Swarm Topology
At CPH.AI, we implement "Mixture of Agents" architectures. Instead of relying on a single monolithic model (like GPT-5) to do everything, we spawn specialized sub-agents orchestrated by a Supervisor Node.
In this topology, the Researcher has access to internet search tools. The Analyst has access to Python execution environments (sandboxed). The Critic compares the output against a rubric. This specialization reduces hallucination rates by 60% compared to zero-shot prompting.
The OODA Loop Implementation
We hard-code the OODA Loop (Observe, Orient, Decide, Act) into the system prompt of the Supervisor Agent via LangGraph. This ensures the system does not just "react" to input, but actively maintains a state of the world, updating it with every tool execution.
Tool-Use Failure Modes
The primary failure mode of agentic systems is not reasoning, but tool interfacing. Agents often hallucinate parameter names or misunderstand API return types.
To mitigate this, we wrap all tools in a Type-Safe Schema (Pydantic). If an agent attempts to call a tool with invalid arguments, the schema validation fails and returns a structured error message to the agent, allowing it to self-correct in the next loop. We call this "Compiler-Guided Reasoning". The agent learns from the stack trace, just as a human developer would.