N6agent: !!top!!

| Layer | Name | Function | |-------|------|----------| | 1 | | Parses raw input (text, images, JSON) into structured intent vectors. | | 2 | Reasoning | Applies chain-of-thought (CoT) and tree-of-thought (ToT) to break the goal into sub-tasks. | | 3 | Planning | Generates a dynamic execution graph (not a fixed DAG). Edges can be rewired mid-task. | | 4 | Tool Selection | Queries a vector DB of available tools (APIs, code functions, web search) and selects the optimal set. | | 5 | Execution | Runs selected tools in parallel or serially with error handling and timeout management. | | 6 | Reflection | Evaluates outcomes against the original goal. If criteria aren’t met, loops back to Layer 2 with new context. |

If your current agents fail as soon as an API changes or a PDF layout shifts, N6Agent is worth exploring. Have you tested N6Agent in production? Share your experiences or questions in the comments below. n6agent

agent = N6Agent( llm="gpt-4o", tools=tools, memory_type="long_term", max_reflections=3 ) | Layer | Name | Function | |-------|------|----------|

In the rapidly expanding ecosystem of AI agents, most systems fall into two categories: simple chatbot wrappers or complex, brittle automation scripts. N6Agent emerges as a hybrid architecture designed to bridge the gap between conversational AI and autonomous, goal-driven execution. Edges can be rewired mid-task

The name "N6" denotes the six core cognitive layers that the agent processes sequentially for each task (see architecture below). N6Agent processes every user request through six distinct layers: