What is agentic reasoning in AI?

Your support team gets a Slack message: "Customer X wants pricing for our enterprise plan with custom SLA terms." Simple request, right? But the work isn't simple. Someone needs to research what competitors charge for similar tiers, draft a comparison showing where you have advantages, update the internal strategy doc with the new data point, and loop back with a recommendation.

If you've ever tried to automate these workflows using AI, you'll know that LLMs handle the Q&A part well. Ask "What's our enterprise pricing?" and they'll answer from training data. But they can't work through that full workflow autonomously. They wait for your next prompt instead of figuring out what to do next on their own.

Agentic reasoning changes this. Instead of chatbots that wait for prompts, you get systems that break down complex goals, retrieve information from the right sources, make decisions about what to do next, and execute actions without constant human intervention.

This article explains how agentic reasoning works, which architectural approaches matter for production systems, and what infrastructure you need when demos become deployments.

What is agentic reasoning?

Agentic reasoning is an AI architecture where systems break down complex goals, choose the right tools, execute actions, and adapt based on results without waiting for your next prompt at each step.

Unlike traditional chatbots that generate answers and stop, agentic systems figure out what information they need, retrieve it from the right sources, and work through problems autonomously.

Here's what makes agentic reasoning different from traditional chatbots:

1. Goal-directed autonomy: Agents establish objectives, deconstruct them into actionable steps, and make real-time decisions without explicit instructions at each phase.
2. Dynamic tool orchestration: Native ability to select and invoke appropriate tools at runtime. Need to check inventory? The agent calls your inventory API. Need to calculate projections? It runs code.
3. Multi-step planning: Agents break complex goals into subtasks, run them in sequence or parallel, and replan based on what they find.
4. Contextual memory: Agents store past interactions and plan future actions, giving you personalization and context-aware responses impossible in stateless architectures.
5. Environment interaction: Direct interaction with digital environments, tools, and APIs without human mediation, adapting in real time to changing conditions.
6. Iterative reasoning-action cycles: Agents operate in loops: thinking, acting, observing results, then deciding what to do next based on what they learned. This pattern is called the ReAct paradigm (Reasoning plus Acting).

Instead of chatbots that wait for your next prompt, you get systems that work through problems on their own.

How agentic reasoning works

Agentic reasoning operates through iterative loops where agents think, act, observe results, and decide what to do next. This pattern (called the ReAct paradigm: Reasoning plus Acting) lets agents adapt their approach as they work through problems. Traditional approaches keep reasoning and action separate. Agentic systems interleave them.

Each cycle works through four steps: the agent analyzes what it knows, picks tools to fill information gaps, processes results, and evaluates whether it's achieved its goal. Each step informs the next.

1. Reasoning

Before taking action, the agent generates reasoning traces: internal monologue that explains its current understanding and identifies what information it's missing. These traces are LLM-generated text that explicitly guide which tools the agent should use next. The agent analyzes its current knowledge state and figures out what questions need answering before it can proceed.

1. Execution

Based on its reasoning, the agent dynamically selects specific tools from its available inventory. Tools can be anything from APIs for checking inventory and database queries for retrieving customer data, to code execution for running calculations or search engines for finding external information. The agent picks the right tool for each step without human intervention.

1. Integration

After executing a tool, the system captures the results and integrates them into the agent's context. This updated context becomes the foundation for the next reasoning phase. The agent doesn't just collect information; it actively processes what it finds and uses that to inform its next decision.

1. Iteration

The agent evaluates whether its goal has been achieved. If not, the cycle repeats with new reasoning traces, new tool selections, and new observations. This continues until the agent determines it has sufficient information to complete its task. This mechanism lets agents make stepwise decisions through verbal reasoning that explicitly guides tool selection at each phase.

Approaches to agentic reasoning

Different problems need different reasoning strategies. Each of these six strategies handles a different type of complexity, so pick the one that best suits your needs.

Chain-of-Thought (CoT)

This is the baseline approach that makes everything else possible. Chain-of-Thought works by breaking down complex problems into explicit intermediate steps. Instead of jumping straight to an answer, the model generates a series of intermediate reasoning steps. This process of showing your work dramatically improves how large language models handle complex reasoning.

When you'd use CoT: Problems with clear sequential dependencies work best here. Think math problems, logical puzzles, or anything where you need to show step-by-step reasoning to reach a conclusion.

ReAct

ReAct takes Chain-of-Thought further by letting agents interact with external tools while they reason. The name combines "reasoning" and "acting" because the agent alternates between thinking about what to do next and actually doing it.

While interacting with external environments like search engines, databases, and APIs, agents generate verbal reasoning traces that explain their decisions. The architectural design follows an interleaved loop where each thought leads to an action, which produces an observation that informs the next thought.

When you'd use ReAct: This approach works when your agent needs to query APIs, search databases, or adapt its strategy based on what it discovers. If your workflow involves looking things up or checking external systems, ReAct gives you that capability.

Self-consistency

Sometimes you need more confidence in your answers than a single reasoning path can provide. Self-Consistency is a decoding strategy that generates multiple diverse reasoning paths for the same problem, then selects the most consistent answer by marginalizing out the sampled reasoning paths.

The trade-off is computational cost because you're running multiple sampling passes, but that's justified when accuracy matters more than speed.

When you'd use Self-Consistency: High-stakes decisions where you can't afford to be wrong. When accuracy justifies the extra computational cost, Self-Consistency gives you that additional layer of confidence.

Tree of Thoughts (ToT)

Linear reasoning assumes you'll get it right the first time. Tree of Thoughts acknowledges that sometimes you need to explore multiple paths and backtrack when you hit dead ends. It generalizes Chain-of-Thought by providing exploration over coherent units of text that serve as intermediate reasoning steps. Think of it like planning several moves ahead in chess, where you explore different branches before committing to a strategy.

When you'd use ToT: Problems that require exploring multiple solution paths with strategic backtracking. When there's no obvious linear path to the answer, ToT lets your agent try different approaches and learn from what doesn't work.

Reflexion

Agents need to learn from their mistakes, but traditional reinforcement learning requires weight updates that aren't always practical. Reflexion takes a different approach by having agents learn through linguistic feedback instead. After completing a task, agents verbally reflect on what worked and what didn't, then store these reflections in an episodic memory buffer. On subsequent attempts, they can reference these past experiences to make better decisions without any model retraining.

When you'd use Reflexion: Scenarios where you need iterative improvement over multiple attempts. If your agent will face similar problems repeatedly and has clear success or failure signals, Reflexion helps it get better over time.

Graph of Thoughts (GoT)

The most sophisticated approach on this list, Graph of Thoughts handles problems where information flows in complex, non-linear patterns. While Tree of Thoughts explores branching paths, Graph of Thoughts lets you model arbitrary relationships between different pieces of information. It advances prompting by modeling information generated by an LLM as a graph structure, where units of information become vertices and the relationships between them become edges.

When you'd use GoT: Problems that call for information synthesis from multiple branches with non-hierarchical reasoning patterns. When your reasoning needs to loop back on itself or connect ideas in ways that don't fit a simple tree structure, GoT provides that flexibility.

Agent-to-agent architecture

The reasoning strategies above focus on individual agents. But production systems often need multiple agents working together, and that's where agent-to-agent (or multi-agent) architecture comes in.

In multi-agent systems, specialized agents collaborate to solve problems that would overwhelm a single agent. One agent might handle research while another drafts content, a third reviews for accuracy, and a fourth manages the overall workflow. Each agent brings its own reasoning capabilities, tool access, and memory to the task.

Here's how multi-agent architectures typically work:

• Orchestrator patterns: A supervisor agent delegates tasks to specialized worker agents, collects their outputs, and synthesizes final results. The orchestrator handles coordination while workers focus on their domain expertise.
• Peer-to-peer patterns: Agents communicate directly with each other, passing context and intermediate results through shared memory or message passing. This works well when agents need to iterate on each other's work.
• Hierarchical patterns: Multiple layers of agents, where high-level agents set strategy and lower-level agents handle execution. This mirrors how human organizations divide work between planning and doing.

The infrastructure requirements multiply with multi-agent systems. Each agent needs memory for its own context, but agents also need shared state to coordinate. You need message passing for agent-to-agent communication, and you need this to happen fast enough that the system feels responsive. When one agent queries another, latency compounds across every hop in the chain.

Multi-agent architectures also introduce new failure modes. What happens when one agent in the chain fails? How do you debug issues that span multiple agents? How do you prevent agents from getting stuck in loops or contradicting each other? These operational challenges become the bottleneck long before reasoning quality does.

Applications of agentic reasoning

Agentic AI has moved into real-world application across multiple industries. Industries like the following are finding use cases where autonomous agents solve problems and deliver measurable results.

Software development

Coding agents can handle the repetitive parts of software development so devs can focus on harder problems. These agents understand your codebase well enough to suggest relevant code completions, debug errors by tracing through logs and stack traces, and manage CI/CD pipelines by catching issues before they hit production. The result is faster development cycles and fewer bugs making it to users.

Healthcare & finance

Healthcare organizations use agents to reduce the administrative workload that takes time away from patient care. Agents handle insurance claim appeals by gathering the right documentation and preparing responses, speed up prior authorization by collecting medical records automatically, and answer routine patient questions around the clock. In financial services, agents help analysts search through massive document repositories and keep compliance documentation current as regulations change, reducing the manual effort required to stay compliant.

Customer service

In customer service, AI agents go beyond reactive support to detect and solve problems before customers even notice them. When a network outage occurs, agents can identify affected customers, send notifications, process refunds or credits, and escalate complex cases to human support staff. This proactive approach reduces support tickets and improves customer satisfaction by addressing issues faster than traditional support workflows allow.

Enterprise IT operations

IT teams use agentic reasoning to shift away from putting out fires to preventing them in the first place. Agents monitor system performance and adjust configurations as needed, analyze patterns to predict when hardware might fail, and investigate security threats by correlating events across multiple systems. This means fewer 3 AM pages for your ops team and more stable systems for your users.

Data analysis

Organizations are making their data more accessible to people who don't write SQL for a living. Agents translate questions like "show me sales trends by region" into database queries, generate charts and dashboards automatically, and explain what the data means in plain language. This makes data analysis available to more people in your organization without requiring them to learn complex query languages or understand database schemas.

Building agentic AI that ships

Agentic reasoning marks a real shift from chatbots to autonomous systems. Demo agents impress investors. Production agents require infrastructure that handles dual-memory architectures, sub-millisecond retrieval, semantic understanding, and thousands of concurrent reasoning loops.

Redis for AI provides fast vector search with sub-millisecond latency for RAG apps. Your agents maintain context within conversations and recall relevant information across sessions. Redis supports both through native checkpointers for conversation state and stores for persistent agent knowledge stored as JSON docs.

For multi-agent systems, Redis adds the coordination layer you need. Redis Streams handle task queues and workflow orchestration, while pub/sub provides real-time messaging between services.

Redis works with leading AI frameworks through native integrations. Your agents can use Redis without custom integration work. The checkpointers, stores, and vector search features plug directly into the frameworks you're already using.

Managing separate databases for vectors, agent memory, and caching means multiple vendors, APIs, and failure points. Redis consolidates everything into one product, reducing operational complexity while delivering the sub-millisecond latency that agents demand.

Ready to build? Try Redis free to see how it works with your workload, or talk to our team about optimizing your agent infrastructure.