The Reasoning Engine: How Agents Plan Actions

How Reasoning Works in Practice

When an agent receives a task, the reasoning engine does not simply generate a response. It evaluates the current state, identifies what information is missing, determines which tools could provide that information, and selects the most promising action. This process happens on every turn of the agent loop, with the model receiving the full conversation context and generating the next step based on everything that has happened so far.

The reasoning process is fundamentally different from how humans reason. The model does not maintain a persistent internal state or build a mental model of the problem. Instead, it processes the entire context as a single input and generates a continuation. The quality of reasoning depends on how well the context represents the problem state: a clear, well-organized context leads to better decisions than a cluttered, ambiguous one. This is why context management and prompt engineering are so important to agent performance.

Modern reasoning engines benefit from extended thinking capabilities. Models like Claude can use structured thinking to work through complex problems before committing to an action. This internal deliberation allows the model to consider multiple approaches, evaluate tradeoffs, and identify potential issues before they become problems. Extended thinking is especially valuable for tasks that require multi-step planning, where the cost of choosing the wrong first step is high because it cascades through all subsequent steps.

The ReAct Pattern

ReAct (Reasoning and Acting) is the dominant reasoning pattern in production agent systems. The pattern interleaves reasoning steps with action steps in an alternating sequence. On each turn, the agent first produces a reasoning trace explaining its current thinking, then generates an action (typically a tool call), receives the result, and uses that result to inform the next reasoning step.

The reasoning trace serves multiple purposes. It helps the model organize its thinking before committing to an action, reducing errors caused by premature decisions. It provides observability for developers and operators who need to understand why the agent made specific choices. And it creates a chain of documented decisions that can be reviewed, audited, and used to improve the agent behavior over time.

A typical ReAct turn looks like this internally: the model thinks "I need to find the user account details. The user provided an email address, so I should search the database by email. The search_users tool accepts an email parameter." Then it generates a tool call to search_users with the email parameter. The tool returns the user record, and the model reasons about what to do with that information on the next turn.

ReAct handles uncertainty well because each action provides new information that the model can use to adjust its approach. If a search returns no results, the model can try alternative search strategies. If an API returns an error, the model can reason about the error and choose a different path. This iterative refinement is one of the key advantages of the ReAct pattern over approaches that try to plan everything upfront.

Plan-and-Execute Strategies

For complex tasks with many steps, generating a complete plan before executing any actions can improve efficiency and coherence. The plan-and-execute pattern separates planning from execution: a planning phase produces a structured list of steps, and an execution phase carries out each step in order, optionally revising the plan when new information emerges.

Planning upfront is most valuable when the task structure is predictable and the steps are largely independent. Writing a research report involves well-known phases (gather sources, read and extract key points, organize themes, draft sections, review and edit) that can be planned before any work begins. The plan gives the agent a roadmap that prevents it from getting lost in details or forgetting important steps.

The weakness of upfront planning is rigidity. The plan is based on assumptions about what the agent will find during execution, and those assumptions are often wrong. A research plan might assume that relevant data is available in a specific database, but the data might be missing, outdated, or in a different format than expected. Rigid plan execution in the face of unexpected conditions wastes time and resources on steps that no longer make sense.

Dynamic replanning addresses this weakness by allowing the agent to revise its plan based on execution results. After each step, the agent evaluates whether the remaining plan is still valid. If not, it generates a revised plan that accounts for the new information. This adaptive approach combines the coherence benefits of planning with the flexibility of reactive execution.

Chain-of-Thought Reasoning

Chain-of-thought (CoT) reasoning prompts the model to show its work by breaking complex problems into intermediate steps. Instead of jumping directly from question to answer, the model produces a sequence of reasoning steps that build toward the conclusion. This approach significantly improves accuracy on tasks that require multi-step logic, mathematical reasoning, or careful analysis of complex scenarios.

In agent systems, chain-of-thought reasoning is used at decision points where the agent needs to evaluate multiple options. Should the agent search for information first, or does it already have enough context to proceed? Should it use the primary API or a fallback? Is the task complete, or are there remaining subtasks? These decisions benefit from explicit reasoning that weighs the evidence before committing to an action.

The cost of chain-of-thought reasoning is additional tokens. Every reasoning step consumes tokens from the context window and adds to the total cost of the interaction. For simple, routine decisions, the overhead of explicit reasoning is not justified. Production agents typically use full chain-of-thought reasoning for complex decisions and faster, more direct processing for routine ones.

Reasoning Under Uncertainty

Real-world tasks involve uncertainty at every level. The agent may not know if a tool will succeed, whether the data it found is accurate, or if its current approach is the most efficient one. How the reasoning engine handles uncertainty determines whether the agent is robust or brittle.

Good reasoning engines maintain awareness of confidence levels. When the agent retrieves information from an external source, it considers the reliability of that source. When it makes a decision based on incomplete information, it acknowledges the uncertainty and plans for contingencies. When it takes an action with irreversible consequences (sending an email, deleting data), it applies higher standards of confidence before proceeding.

Exploration versus exploitation is a fundamental tension in agent reasoning. Should the agent continue gathering information (exploration) or act on what it already knows (exploitation)? Gathering more information reduces uncertainty but costs time and resources. Acting on incomplete information is faster but risks errors. The best reasoning engines balance this tension by estimating the value of additional information against the cost of acquiring it.

Multi-Model Reasoning

Advanced agent systems use multiple models within the reasoning engine, routing different types of reasoning tasks to different models. A frontier model handles complex planning, multi-step analysis, and novel problem-solving. A mid-tier model handles routine tool selection and straightforward decision-making. A small model handles classification, extraction, and formatting tasks. This multi-model approach optimizes the cost-quality tradeoff by using expensive reasoning only where it adds value.

The routing logic can be rule-based (specific task types always go to specific models), classifier-based (a lightweight model classifies the difficulty of each reasoning step and routes accordingly), or adaptive (the system starts with a smaller model and escalates to a larger one if the smaller model expresses uncertainty or produces a low-confidence result). Adaptive routing provides the best cost savings because it only uses expensive models when cheaper ones are insufficient.

Consensus reasoning uses multiple models to reason about the same problem independently, then compares their conclusions. If all models agree, the answer is likely correct. If models disagree, the disagreement highlights uncertainty that warrants additional investigation or human review. This approach improves reliability for high-stakes decisions where the cost of an error justifies the cost of multiple model calls.