Maturity framing for how much autonomy, tooling, and orchestration a system exhibits (PDF 232–235).
● We’re also assuming that the agent will never call a tool that doesn’t exist, and that all tools will succeed silently. In a production-grade system, you’d want to:
● Add more robust parsing (e.g., structured prompts with JSON outputs or function calling).
● Include tool validation, retries, and exception handling.
● Use guardrails or output formatters to constrain what the LLM is allowed to emit. But for the purpose of understanding how ReAct-style loops work under the hood, this is a clean and minimal place to start. It gives you complete transparency into what’s happening at each stage of the agent’s reasoning and execution process. This loop demonstrates how a simple agent can think, act, and observe, all powered by your own Python + local LLM stack. 5 Levels of Agentic AI Systems Agentic AI systems don't just generate text; they can make decisions, call functions, and even run autonomous workflows. The visual explains 5 levels of AI agency - from simple responders to fully autonomous agents.
1) Basic responder
A human guides the entire flow. The LLM is just a generic responder that receives an input and produces an output. It has little control over the program flow.
2) Router pattern
A human defines the paths/functions that exist in the flow. The LLM makes basic decisions on which function or path it can take.
3) Tool calling
A human defines a set of tools the LLM can access to complete a task. LLM decides when to use them and also the arguments for execution.
4) Multi-agent pattern
A manager agent coordinates multiple sub-agents and decides the next steps iteratively. A human lays out the hierarchy between agents, their roles, tools, etc. The LLM controls execution flow, deciding what to do next.
5) Autonomous pattern