Building Autonomous AI Agents
Agentic Reasoning Loops
From Words to Actions
Large Language Models are powerful text generators, but their real potential is unlocked when they can act on the world. An AI agent does just that. It's not just a passive model; it's an active problem-solver. At the heart of every agent is a reasoning loop, a core process that allows it to think, act, and learn from feedback.
The ReAct Cycle
One of the most effective reasoning loops is the ReAct pattern, which stands for Reason and Act. It's an iterative cycle that mimics how humans often solve problems. Instead of trying to figure out every single step in advance, an agent using ReAct takes one step at a time, assesses the outcome, and then decides what to do next.
The process is a continuous loop with three distinct phases: Thought, Action, and Observation.
By externalising its reasoning into a clear 'Thought' step, the agent's logic becomes traceable. We can read through its decision-making process, which is invaluable for debugging and understanding why it took a certain path. This trace helps us see how the agent grounds its logic in the feedback it receives, reducing the chance of it hallucinating or getting stuck on a wrong assumption.
Think of cooking a complex dish for the first time. You read a step (Thought), chop the onions (Action), and see if they're properly browned (Observation) before moving on to the next instruction. You don't perform every action from start to finish without checking your progress.
Here's a simplified trace for an agent tasked with finding the current UK prime minister's age:
Thought: I need to find out who the current UK prime minister is and then find their date of birth to calculate their age. I will start by searching for the current UK prime minister.
Action: search("current UK prime minister")
Observation: The search result says Keir Starmer is the current prime minister.
Thought: Now that I know the prime minister is Keir Starmer, I need to find his date of birth.
Action: search("Keir Starmer date of birth")
Observation: The search result says he was born on 2 September 1962.
Thought: I have the date of birth. I can now calculate his age based on the current date and provide the final answer.
Action: calculate_age("1962-09-02")
Observation: 61
Thought: I have the final answer.
Action: finish("Keir Starmer is 61 years old.")
Iterate or Plan Ahead?
The ReAct pattern is a form of iterative reasoning. The agent continuously refines its approach based on new information. This is powerful but not the only way an agent can operate. An alternative is the Plan-and-Execute approach.
In this model, the LLM first creates a complete, step-by-step plan to achieve the goal. Once the plan is finalised, the agent executes each step in sequence without stopping to re-evaluate. The reasoning happens entirely upfront.
| Feature | ReAct (Iterative Reasoning) | Plan-and-Execute |
|---|---|---|
| Flexibility | High. Can adapt to unexpected results or errors mid-task. | Low. Cannot easily deviate from the initial plan. |
| Use Case | Complex problems where the path isn't clear; dynamic environments. | Predictable tasks with well-defined steps. |
| Execution Speed | Slower. Requires multiple LLM calls for each thought cycle. | Faster. Only one initial LLM call for planning. |
| Robustness | More robust to errors, as it can self-correct. | Brittle. A single failure in the plan can derail the whole task. |
Choosing between these patterns depends on the task. For booking a multi-stop flight with changing availability, the adaptive nature of ReAct is ideal. For a simple task like converting a file from one format to another, a straightforward Plan-and-Execute agent is more efficient.
Managing the Loop
A reasoning loop can't run forever. The agent needs clear exit conditions to know when its job is done. This is usually managed by a special 'finish' or 'final_answer' action. When the agent's internal thought process concludes that it has successfully achieved the user's goal, it calls this action to terminate the loop and deliver the result.
But what if things go wrong? LLMs are non-deterministic, meaning they might not give the same output every time, even with the same input. This can cause an agent to get stuck in a repetitive loop or fail to make progress. To handle this, we can implement safeguards:
- Maximum Iterations: A hard limit on the number of thought-action-observation cycles. If the agent doesn't solve the problem within, say, 15 steps, it stops and reports an error.
- Timeout: A time limit for the entire task. If the agent takes too long, it's terminated.
- Error Handling: Programming the agent to recognise when a tool returns an error. Its next 'Thought' can then be about how to recover from that specific error, rather than blindly retrying the same failed 'Action'.
An effective agent combines observation, reasoning, action, and reflection in a continuous loop that can solve complex problems beyond what a simple LLM call could achieve.
By managing the loop with clear exit conditions and robust error handling, we can build reliable agents that harness the reasoning power of LLMs to accomplish meaningful tasks.
What is the primary characteristic that distinguishes an AI agent from a standard Large Language Model (LLM)?
What are the three distinct, sequential phases of the ReAct reasoning loop?