What Are AI Agents?

• AI agents defined
• The evolution of AI agents
• How do AI agents work
• Key components of an AI agent
• How AI agents reason
• AI agents compared to other AI
• Key benefits of AI agents
• AI agent best practices
• Types of AI agents
• The future of AI agents

By Magulan Duraipandian, Sr. AI Solutions Technical Evangelist - Salesforce

An agent is autonomous in how it reasons and acts but that autonomy is bounded. It works inside the rules and guardrails you set, which is why autonomous does not mean uncontrolled.

The difference from traditional software is like the difference between a recipe and a cook. Conventional automation follows a recipe: give it the same input and it runs the same fixed steps; anything the recipe didn't anticipate breaks it. An agent works more like a cook who knows the goal and improvises the path. The exact steps vary, but the outcome stays consistent. That flexibility comes from the large language model (LLM) at the agent's core, and it cuts both ways. Left ungoverned, the same model that reasons fluently will also skip steps, ignore rules, or state something false with full confidence. So the most capable agent is not the most autonomous one. It is the one that strikes the right balance between flexibility and control: free to reason where judgment helps, held to a fixed path where a task has to run the same way every time.

That balance is why an agent's independence should be a setting, not a fixed trait. Answering a product question can run loosely. A payment or an identity check is pinned to an exact, auditable sequence the agent cannot skip or reorder. According to Salesforce's State of IT: AI and App Development report, 83% of developers say AI agents are fundamentally changing how organizations operate, and 78% worry their business will fall behind if they don't adopt them.

This page covers what AI agents are, how they work, the types in use today, where organizations are putting them to work, and what it takes to deploy them responsibly.

An AI agent is a type of software that uses generative AI to decide what to do next and how to do it. Given a goal, it reasons through the actions needed to reach it, works out what data it requires, and then takes action, with or without a person in the loop.

That is what separates an agent from the generative AI tools most people already know. A tool that answers a question or drafts text responds to a single prompt and is stateless: it doesn't retain memory between turns, and it doesn't take actions of its own. An agent does more with the same kind of model. It plans, using the large language model (LLM) to break a goal into steps. It acts, calling APIs and querying the systems it connects to. AI agents can maintain an ongoing record of context, actions and history.

Early automation ran on rule-based systems: explicit if-this-then-that logic a person wrote out in advance. These systems were reliable inside their rules and helpless outside them, because they could only follow steps someone had already anticipated. Change the situation, and the rules broke.

Narrow AI came next. Machine-learning models could recognize a pattern, classify an image or predict an outcome far better than hand-written rules, but each model did one job. A fraud classifier could not book a meeting, and a recommendation engine could not answer a question. The intelligence was real, and it was boxed into a single task.

The large language model was the missing piece. An LLM can take a request stated in plain language, reason about the actions needed to reach it, work out what data it requires, and choose a different path when it learns something mid-task. That is the capability both earlier approaches lacked: rule-based systems needed every step spelled out, and narrow models could not reason beyond the one thing they were trained to do. A reasoning layer that can plan its own path through a set of tools and data sources is what turns them into an agent.

An AI agent works by running a loop that mirrors how a person handles a task: it takes in the situation, decides what to do, then acts, learning from the result before the next step. The three stages of that loop — observe, plan, act — are not a one-way sequence but a cycle the agent repeats until the goal is met.

The agent gathers what it needs to understand the situation, pulling from user inputs, connected data sources, APIs, real-time signals and other system integrations. It draws on memory as it does this: short-term memory holds the immediate context of the task in progress, while long-term memory carries knowledge accumulated across earlier sessions. Rather than hold all of this as fixed state, the agent assembles the relevant context freshly each turn, retrieving the specific data a step needs at the moment it needs it. That is what lets it reason about what is actually happening now, informed by what came before, instead of reacting to a single input in isolation.

The large language model acts as the reasoning engine. It decomposes the goal into subtasks, weighs which tools or data sources each subtask needs, and selects a path toward the outcome. At each decision point it works out whether it has enough to act, whether it needs to retrieve or update information first, or whether it has to ask the person for more detail before continuing. Planning is not a one-time step: as new information arrives or circumstances change, the agent re-evaluates and revises the plan rather than committing to a route that no longer fits.

The agent executes the plan by calling tools, invoking APIs, updating enterprise systems or handing work to other agents. The result of each action is captured and fed back into the loop, so the agent re-evaluates against the goal and decides whether to take another action, gather more information, or respond. Before it delivers a response, it runs a grounding check: the answer has to be based on what the actions actually returned, free of unverified or hallucinated claims. If that check fails, the agent tries again rather than pass along a shaky answer, and if it still cannot ground the response, it says so instead of guessing.

Each pass through the loop feeds the next. What the agent learns while acting sharpens its next observation, and the cycle repeats until the goal is met.

Five components work together to make an agent function:

• Foundation model. The large language model at the agent's core, and the source of its reasoning: it interprets a request in plain language and decides what to do next and how to do it. This is where the agent's flexibility comes from, and also its unpredictability. The model reasons fluently but probabilistically, which is why the other four components exist to direct and constrain it.
• Memory. What lets the agent build on what it knows instead of starting cold each turn. Rather than hold everything as fixed state, the agent re-assembles the relevant context each turn, drawing on two kinds of memory: information set at the start of a session and held steady for its duration, such as a known customer's account details, and working state that changes as the task runs, such as whether the customer's identity has been verified. Short-term memory carries the task in progress; long-term memory carries what was learned in earlier sessions.
• Persona. The designed personality that tells the agent who it is and how to express itself. This is not cosmetic: users assign a personality to a conversational agent within seconds, so every interaction becomes a brand interaction whether it was designed or not. Persona is a system, not a one-line instruction, and the traits that define an agent shape how it reasons about a request, not only how it phrases the reply. It also sets expectations: if an agent talks like a human, users may expect it to reason like one.
• Tools and integrations. What let the agent act in the world rather than only reason about it. Through them the agent calls external actions, such as API requests and database lookups, and writes updates back to the enterprise systems it works across. This is the difference between an agent that recommends an action and one that performs it.
• Planning module. The logic that turns a goal into an ordered sequence of steps and the tool calls that carry them out. It does not fix the route once and follow it blindly: after each result, the agent decides again what to do next, re-planning when a step fails or conditions change.

The way an agent reasons is not purely open-ended. The most reliable approach combines two modes in a single system: probabilistic reasoning from the language model, which handles open-ended judgment and natural language, and deterministic, rule-based execution, which handles the steps that must run the same way every time. Deterministic automation and agentic reasoning are not an either/or choice; the framework runs them together, and lets you dial how much of each a given task uses, depending on whether it needs flexibility or strict reliability.

Within that framework, a single request moves through a consistent loop. The agent first classifies what is being asked to determine which capability handles it. It then runs any fixed business rules that apply before it does open-ended reasoning, so mandatory steps are never skipped or reordered. It calls actions to retrieve or update the data the task needs, and after each one it decides again what to do next: run another action, ask for more information, or respond. Before it returns an answer, it runs a grounding check, confirming the response is based on what the actions and rules actually produced rather than on an unverified assumption.

Artificial intelligence covers several different technologies, and the differences decide what each one can actually be trusted to do. The distinction that matters for buyers is how much a system decides and acts on its own, versus how much it waits for a person to direct each step.

Across these, the AI agent is the only category that autonomously pursues a goal, acts across the systems it connects to, and learns from what each step returns rather than following a fixed script. It can plan, reason, and act, and it can choose a different path based on what it learns mid-task, which is the capability the others lack. That is also why an agent is the wrong tool for a process that never changes: when the steps are always the same, fixed automation is the more reliable choice, and agents earn their keep on messy, unpredictable work.

The value of AI agents comes from pairing the availability and scale of software with judgment that used to require a person.

• Around-the-clock availability. An AI agent doesn't keep business hours. It handles a request at 2 a.m. as readily as one at 2 p.m., which matters most for global operations and for the routine questions that arrive outside a team's shift.
• Scale beyond simple tasks. Rule-based automation and early chatbots were confined to the shallow end of the work. They could handle simple FAQs and low-stakes requests, but anything ambiguous broke them. Because an AI agent interprets the intent behind a request and selects an action from a range of possibilities, it can take on the messier, multi-step work that used to route to a person, and do it for thousands of requests at once.
• Lower cost to serve. An agent absorbs routine, high-volume work without adding staff, so the cost per interaction falls as volume rises. Well-built agents compound this by resolving fixed steps with deterministic logic instead of a fresh model call every time, which reduces the number of model invocations and lowers operational cost.
• Consistent outcomes without rigidity. A scripted system behaves like a novice cook bound to a recipe: any deviation from the written steps ends in failure. An AI agent behaves more like a seasoned chef who, given a general understanding of what you want, reaches a consistently good result even when the exact steps vary from one time to the next. The practical benefit is reliable outcomes with the flexibility to handle inputs phrased in ways no one scripted for.
• More accurate, grounded answers. Left to its training data alone, a language model answers from static, general knowledge that can be out of date or simply wrong, and when it is unsure it will fabricate a confident answer anyway. Agents counter this through grounding: retrieving accurate, current information from a connected knowledge source at the moment of the request and answering from that rather than from memory. Grounding is the primary defense against invented answers.
• Personalization at scale. Because an agent assembles the relevant context for each request, its response is pertinent to the specific user and situation instead of generic. The same system that serves thousands of people can tailor each interaction to one.
• Improved customer satisfaction. Faster answers, grounded in the right information and available whenever the customer reaches out, add up to a better experience. In Salesforce's State of Commerce report, 42% of organizations using AI said it has had a positive impact on customer experience. As agents take on more of the front-line interaction, that experience gain scales along with them.

Spinning up an impressive agent demo is the easy part. The organizations that get real value from AI agents are the ones that treat deployment as the start of a continuous lifecycle — plan, build, test, evaluate, observe, iterate — rather than a finish line. Most of the difference between an agent that thrives in production and one that falls apart under real-world complexity comes down to the disciplines below.

• Assign ownership across the whole lifecycle, not just the build. The most common failure isn't technical — it's a gap in accountability. Once an agent is live, someone has to own testing it against edge cases, someone has to evaluate its performance over time, and someone has to decide which metrics matter. When those roles go undefined, the agent drifts and no one is answerable. Decide up front who owns each phase, so humans and agents genuinely work side by side.
• Define measurable success criteria and a baseline before you build. Align the agent's capability to a specific business outcome with a concrete, measurable target — a goal like "cut average resolution time by 30%," not "improve service." Then record your current performance on that metric first. Without a baseline, you can't prove the agent improved anything, and without a measurable target, you can't tell whether it's working at all.
• Start with a focused use case and expand once it's proven. Trying to solve every problem with one agent on day one is a reliable way to fail. Begin with a narrow, high-value task that has clear, measurable outcomes — order status, password resets, common questions — validate it in production, then widen the scope. Range earned through proof beats range assumed on day one.
• Ground the agent and enforce the guardrails that can't be skipped. Connect the agent to verified, current knowledge and retrieve at the moment of the request, so answers come from trusted data rather than the model's memory. Scope each agent to least-privilege permissions so it can reach the data it needs without exposing what it shouldn't. Where an outcome must be guaranteed, surround the model's reasoning with deterministic logic that forces the required steps to run in order, and build real error handling — fallbacks, timeouts, clear messages — so the agent fails gracefully instead of confidently guessing.
• Test beyond the happy path. The bar for an enterprise agent isn't "does it work?" — it's "does it work reliably, safely, and consistently?" Testing only clean, happy-path scenarios means edge cases surprise you in production. Probe with messy, realistic inputs and phrasing variations, test with the actual permission sets real users will have rather than admin access, and confirm the agent degrades gracefully when a tool call fails or it hits an unexpected condition.
• Diagnose root causes, and route each issue to an owner. Finding that an agent fails is not the same as knowing why. For each recurring problem, determine whether it stems from unclear instructions, a knowledge gap, or a broken action, and route it to the person who can fix that specific cause. Patching surface symptoms without diagnosing the root leaves the underlying failure in place.
• Monitor continuously, and keep a human accountable for consequential actions. Because the underlying model is stochastic, new errors and hallucinations can emerge at any time, so monitoring is an ongoing job, not a launch-day gate. Watch performance in production and set alert thresholds by business impact rather than arbitrary percentages, so genuine problems don't drown in noise. Give each agent a unique identifier and keep detailed activity logs so every action can be attributed and audited, require explicit human approval before high-stakes or irreversible actions, and make sure an operator can interrupt a running agent mid-task.
• Iterate by business impact, and build a feedback loop. Metrics tell you what is happening; reviewing and labeling real interactions tells you why. Feed those insights back into targeted improvements — refined instructions, better knowledge, adjusted logic — and prioritize the issues that move business value rather than chasing perfection on every interaction. As the loop matures, teams often graduate from manual review to AI-assisted evaluation. Each cycle should build on the last.

AI agents are categorized two useful ways: by the job they're built to do, and by how they're architected. The first lens tells you where an agent fits in your organization; the second tells you what it's capable of.

Simple reflex agents operate entirely on fixed condition-action rules. They possess zero memory. As a result, they can't handle any inputs outside their predefined logic parameters. A basic IT support agent demonstrates this. It simply triggers a password reset workflow whenever a user types the exact phrase "locked out" – and fails if the phrasing changes.

Operating in dynamic environments requires context. Model-based reflex agents maintain an internal map of the world. By continuously updating this state as new data arrives, they operate reliably even when visibility is limited. Consider an autonomous warehouse logistics agent. It tracks inventory locations and actively recalculates its picking routes in real time as shelves empty or aisles get blocked.

Reacting to real-time inputs isn't always enough. Goal-based agents plan full sequences of actions to achieve a specific objective. They look ahead before they move. A cybersecurity response agent acts this way. When detecting a breach, it evaluates dozens of potential containment paths and executes the exact sequence required to isolate the network threat.

Reaching an objective is one thing. Doing it efficiently requires complex math. Utility-based agents maximize value across multiple competing criteria rather than just hitting a static target. A programmatic ad-bidding agent operates on this logic. It simultaneously balances click-through probability, fluctuating bid costs, and strict daily budget limits to maximize return on spend.

Static logic decays over time. Learning agents continuously improve through direct experience. They combine a performance element for taking actions with a learning mechanism that evaluates the results. Over time, this feedback loop updates their baseline behavior. Retail recommendation engines rely on this exact structure. They constantly refine their product suggestions based on live user interaction metrics and purchase history.

Black-box algorithms fail in highly regulated environments. Transparency is non-negotiable. Explainable AI (XAI) agents are explicitly designed to expose how and why they make decisions. This capability makes their internal reasoning fully auditable and traceable. A compliance agent uses this framework when it flags a suspicious financial transaction. It doesn't just block the trade. It explicitly highlights the exact sequence of risk signals that triggered the alert.

Massive distributed workflows require structural organization. Hierarchical agents operate in distinct tiers. Higher-level supervisory models break down complex tasks and coordinate lower-level specialist agents for the actual execution. You see this in automated customer service networks. A primary triage agent classifies the underlying intent of an incoming request and instantly dispatches the work to specialized resolution agents.

Single models inevitably hit scaling limits. Multi-agent systems deploy multiple AI entities working together to accomplish goals no solo model could handle. These agents collaborate, coordinate, and sometimes compete. They constantly exchange data to keep the overall architecture aligned. This setup powers modern digital supply chains. Distinct, specialized agents autonomously manage demand forecasting, inventory ordering, and shipping logistics in a continuous loop.

The theoretical taxonomy above translates into highly capable production systems. Here is how modern agents behave right now in live business environments.

• Tool-using agents: They call external APIs, run code, or query databases directly. This capability lets them retrieve live information and complete actions far beyond simple text generation.
• Retrieval-augmented (RAG-powered) agents: These models look up specific data from company documents and proprietary systems. As a result, their responses remain strictly grounded in accurate, current business context.
• Planner or orchestrator agents: Users often submit large or ambiguous requests. The orchestrator breaks these down into manageable steps and logically determines the exact execution order.
• Supervisor or watchdog agents: Quality control requires strict oversight. Watchdogs actively monitor what other agents are doing, verify the outputs, and escalate to a human when necessary.
• Multi-agent collaboration: Distinct AI roles pass data back and forth to solve complex problems. A specialized finance agent and an HR agent will directly exchange information to finalize a shared corporate task.

The next phase of AI agents is collaborative. The first wave put single agents to work on discrete jobs — answer a question, resolve a case, draft a reply. What's emerging now is systems of agents that coordinate with each other, with the tools around them, and with the people they work alongside. Two open standards make that possible: the Model Context Protocol (MCP), which lets an agent reach resources, tools, and prompts across internal and external systems, and the Agent-to-Agent (A2A) protocol, which gives agents a shared language to communicate and collaborate across organizational boundaries.

As that happens, the agent shifts from a solitary tool to a kind of command center. Instead of one agent handling one task, a coordinating agent breaks a goal into parts, routes each to a specialized agent, and assembles the result, orchestrating a workflow the way a manager runs a team. This raises the operational bar rather than lowering it. Once you're running more than a handful of agents, the challenge moves beyond monitoring any single one to guiding, coordinating, and improving all of them as a system.

That trajectory points to a genuinely different way of working: agents treated less like software and more like teammates. In that model, an agent is onboarded much like a new hire — assigned a role, given access to the data and systems it needs, and integrated into the workflows it will run — rather than installed and forgotten. The human role shifts from doing every step to directing the work, setting the goals, guardrails, and escalation points, and staying accountable for the consequential decisions.

None of this waits for a distant breakthrough; the standards and the operating patterns exist now. The organizations that start building the muscle early — grounding agents in their own data, defining where autonomy is safe, and learning to operate a fleet rather than a single bot — are the ones positioned to lead as the technology matures.