Get to Know Reasoning Engines
Learning Objectives
After completing this unit, you’ll be able to:
- Describe what a reasoning engine is.
- List three types of reasoning that reasoning engines use to solve problems.
- List four popular reasoning strategies that guide LLMs.
AI’s Latest Innovation
AI can now do more than just chat. It can actually think through problems, weigh options, and make decisions. To do that–make complex decisions, deliver insights, and respond to the context of the moment–AI agents use reasoning.
But AI works best when you understand how it thinks. In this module, you explore how reasoning engines help advanced AI understand and deliver what you want–whether that’s drafting an email, generating a campaign brief, building a web page, researching competitors, analyzing data, summarizing a call, or another time-saving task.
So, what, exactly, is a reasoning engine?
A Reason to Rely on AI
A reasoning engine is a type of AI that gathers information, follows logical rules, and makes decisions–just like people do when solving problems.
And, like people, these engines typically use three types of reasoning.
-
Deduction: “All fruits have seeds. A mango is a fruit. Therefore, a mango has seeds.” (Starts with a general rule and applies it to a specific case.)
-
Induction: “The last five meetings started late. So, the next one will probably start late too.” (Looks at patterns in past experience to make a general prediction.)
-
Abduction: “The lights are off and no one's answering the door—so they’re probably not home.” (Takes the best guess based on limited clues.)
These approaches make reasoning engines particularly useful. Their reasoning helps AI quickly solve problems that would normally require a thinking human’s perspective and awareness of context, allowing new ways of working at scale.
How LLMs Learn to Reason
Large language models (LLMs) became mainstream in late 2022, but researchers have been experimenting with ways to make them think and plan like humans. The secret? Prompts–carefully written instructions that guide the LLM’s response. When a prompt helps an LLM lay out a logical plan to solve a problem, we call that a reasoning strategy.
Here are four popular strategies.
1. Chain-of-Thought (CoT)
Think of this as teaching the LLM to “show its work.” CoT breaks a tough problem into a series of smaller steps, like a human working through a puzzle. It’s great for math word problems, common-sense reasoning, and other tasks that require logic. Bonus: Engineers can trace each step to see where things might have gone wrong.
2. Reasoning and Acting (ReAct)
ReAct combines reasoning with real-world actions. This strategy doesn’t just rely on what the LLM knows–it interacts, checks information, and refines its answers step by step with user feedback. This leads to fewer “hallucinations” (or wrong answers) and more trustworthy results.
3. Tree of Thoughts (ToT)
Instead of following one single plan, ToT explores many possible paths at each step, like brainstorming multiple options before choosing the best one. This makes ToT powerful for complex challenges like math puzzles, creative writing, or strategic decision-making.
4. Reasoning via Planning (RAP)
RAP takes reasoning a step further by helping the LLM simulate future outcomes. It predicts how actions will play out, explores alternatives, and refines its plan as it goes, much like a human strategist. RAP shines at tasks requiring long-term planning, logical inference, or multi-step problem-solving.
These strategies give LLMs a way to think through problems systematically, rather than just guessing. Whether breaking problems into steps (CoT), interacting with feedback (ReAct), exploring multiple options (ToT), or simulating future states (RAP), each approach makes AI feel a little more like human reasoning–only faster!
How Agentforce Deploys LLM Reasoning
Agentforce is Salesforce’s agentic layer, or smart AI helper. As a complete AI solution, it offers employees and customers a number of ways to chat with agents in plain language, helping teams work faster and customers get answers instantly. Behind the scenes, it uses large language models (LLMs) not just to understand and respond, but also to plan out complex tasks, like a reasoning engine.
Here’s what happens step by step.
- A user types in a request, for example,“Build a webpage.”
- Agentforce sends that input to a secure LLM using a carefully designed prompt. This helps the LLM translate the user’s input into a defined need that the AI understands.
- Once the intent is clear, another prompt asks the LLM to create a plan to get it done.
- The LLM responds with a step-by-step plan. This plan is built only from actions that an agent is allowed to take, ensuring safe and trusted behavior.
- The agent follows the steps, executes the actions in the right order, and delivers the result back to the user.
This process reduces the mental effort required from users. Instead of needing to figure out how to do something, a user can just say what they need—and Agentforce takes care of the how.
Reasoning in Action
Agentforce gives companies a bold new capability: It turns large language models into real-time reasoning engines. That means AI isn’t just answering questions, it’s making sense of complex scenarios, planning next steps, and taking action.
Here are a few examples of what that can look like.
-
Sales feeling sluggish? Agentforce can scan your CRM, spot promising leads, and tee them up for your reps.
-
Deals on the downswing? Agents can flag at-risk opportunities, summarize account histories, and equip managers with quick, clear insights.
-
Overbilling issue? An agent pulls the right records, surfaces helpful troubleshooting steps, and helps resolve the issue quickly.
-
Need to close the quarter strong? Agents can assess customer sentiment, predict deal trajectory, and recommend what to do today to set yourself up for success tomorrow.
In all these examples, Agentforce acts like a semi-autonomous teammate. It’s thinking through problems with LLM-powered logic, responding to natural language, and driving connections between stakeholders.
Let’s Recap
Now that you understand the basics of reasoning engines, it’s easy to see why so many businesses are invested in the technology. Thanks to a human-like way of approaching real-world problems, AI can now stand shoulder-to-server with hardworking teams of professionals and deliver trusted, efficient, and easy solutions to meet your business objectives.
Next, explore the Salesforce reasoning engine, Atlas.