--- title: "What is agentic AI? AI agents explained without the hype" description: "What makes AI agentic: the loop of goal, plan, tool call and check, how an agent differs from a chatbot and a workflow, and why errors compound over many steps." canonical: "https://saascron.com/agentic-ai/" language: "en-US" author: "Ricardo Machado" author_url: "https://saascron.com/author/ricardo-machado/" published: "2026-10-25T07:00:00-04:00" updated: "2026-10-25T07:00:00-04:00" category: "AI & Automation" tags: ["AI agents", "Agentic AI", "Automation", "Llm"] --- # What is agentic AI? AI agents explained without the hype > What makes AI agentic: the loop of goal, plan, tool call and check, how an agent differs from a chatbot and a workflow, and why errors compound over many steps. Every software launch now mentions "agents", and someone on your team has asked whether you need one. You need a definition you can hold a product demo against, not a slogan. Agentic AI is software in which a language model decides its own next step and uses tools to act, in a loop, until a goal is met or a limit stops it. That definition matters because it tells you what to test, what can go wrong and who is accountable. This guide covers the agent loop, the difference between an agent, a chatbot and a workflow, why errors compound over many steps, and what stays your responsibility. For the basics of software delivered as a service, see [What is SaaS?](https://saascron.com/what-is-saas/). ## Agentic AI in plain terms Think of three kinds of help in an office. A receptionist answers questions when asked. A clerk follows a written checklist, step by step, the same way every time. A trusted assistant gets a goal, such as "get the team to the conference for under $1,500 each". The assistant works out the steps alone: checks flights, compares hotels, books, and comes back when something blocks the plan. The receptionist is a chatbot. The clerk is a workflow. The assistant is an agent. Anthropic, which builds language models, draws the line between the last two in [Building effective agents](https://www.anthropic.com/engineering/building-effective-agents) (December 19, 2024). Workflows are "systems where LLMs and tools are orchestrated through predefined code paths". Agents are "systems where LLMs dynamically direct their own processes and tool usage". An LLM (large language model) is the model that reads and writes text. Microsoft's [AI agent shared responsibility model](https://learn.microsoft.com/en-us/azure/security/fundamentals/shared-responsibility-ai-agent) (updated August 26, 2026) adds what makes an agent different from a model that only answers. An agent "invokes tools, calls APIs, writes data, and triggers workflows without a human approving each step". It also plans, keeps memory and holds an identity of its own when it signs in to other systems. ## The agent loop Strip away the branding and an agent runs one loop: 1. **Goal.** A person or a system states what should be true at the end. 2. **Plan.** The model breaks the goal into a next step. 3. **Tool call.** The model calls a tool: a search, an API, a database query, a script. 4. **Check.** The model reads the result and decides whether it moved closer to the goal. 5. **Repeat or stop.** It loops back to step 2, or stops because the goal is met, a limit is hit or it needs a human. Anthropic's guidance puts weight on step 4. An agent should get "ground truth" from its environment at each step, such as a tool result or the output of code it ran. It can pause for human feedback at checkpoints or when it hits a blocker. Stopping conditions, such as a maximum number of iterations, keep the loop under control. The tools are what turn text into action. A model that can only write a reply cannot change anything. A model connected to your CRM, your email and your billing system can. ## Agent, chatbot or workflow The three are often sold under the same label. This table separates them by what each one does, not by what it is called. | Question | Chatbot | Workflow with AI steps | Agent | |---|---|---|---| | Who decides the next step? | The person asking | The designer, in advance | The model, at run time | | Can it change data in other systems? | No, a person acts on the answer | Yes, along fixed paths | Yes, through any tool it has been given | | Number of steps | One reply per question | Fixed | Varies per run | | Cost per run | Predictable | Predictable | Varies with the number of loops | | Typical failure | A wrong answer a person can spot | A broken path or bad input | A wrong action, repeated or chained | | Fits best | Questions and drafts | Repeatable tasks with known steps | Open-ended tasks where the steps cannot be listed in advance | Read the last row first. If you can write the steps down, a workflow is cheaper to run and easier to test. Anthropic recommends "finding the simplest solution possible, and only increasing complexity when needed". It adds that for many applications, "optimizing single LLM calls with retrieval and in-context examples is usually enough". ## Why errors compound Anthropic states the trade-off: "The autonomous nature of agents means higher costs, and the potential for compounding errors." The arithmetic shows why. **Example:** a hypothetical agent that gets each step right 95% of the time, with steps treated as independent. - 2 steps: 0.95 x 0.95 = 0.9025, so 90.3% of runs finish without an error. - 5 steps: 0.95^5 = 0.774, so 77.4%. - 10 steps: 0.95^10 = 0.599, so 59.9%. - 20 steps: 0.95^20 = 0.358, so 35.8%. A step that looks reliable on its own fails four runs in ten once it is chained ten times. Now add a working check. Suppose the agent catches a bad step and retries it once, and the retry also succeeds 95% of the time. A step then fails only if both attempts fail: 0.05 x 0.05 = 0.0025. Each step succeeds 1 - 0.0025 = 0.9975 of the time. Over 10 steps: 0.9975^10 = 0.975, so 97.5% of runs finish cleanly. The model did not get smarter between the two calculations. The check did the work. That is why step 4 of the loop, and the "ground truth" behind it, decides whether an agent is usable. ## What stays your responsibility Microsoft's agent model lists what you keep whatever product you use. You own your data and the agent's identity, including the scope of every credential it holds. You decide which actions it may take and which need a human, and you answer for what it does. It recommends human approval for "high-impact, irreversible, or sensitive actions" such as writes, deletes, payments, production changes and external sends. The same document names the risks that are new with agents. Prompt injection, where text in a web page, document or email hijacks the agent, can now trigger actions instead of a bad answer. "Excessive agency" means the agent has more tools or permissions than the task needs. Its rule of thumb fits on one line: "Autonomy never reduces accountability." In practice, an agent that reads your support inbox and can issue refunds is a support tool and a payment tool at once. Treat its permissions the way you would treat a new employee's on day one. If you connect an agent to your SaaS apps, the plumbing is the same as any integration: APIs, OAuth grants and webhooks, covered in [SaaS integration](https://saascron.com/saas-integration/). Rolling it out follows the same discipline as any new system, see [SaaS implementation](https://saascron.com/saas-implementation/). ## Common mistakes 1. **Calling every chatbot an agent.** A system that only replies cannot act, so it carries different risks and costs. Why it happens: vendors use "agent" as a marketing label. Instead: ask which tools it can call and whether it decides its own next step. 2. **Choosing an agent for a task with fixed steps.** You pay for variable loops and accept compounding errors where a workflow would do. Why it happens: agents are what is being launched. Instead: write the steps down; if you can, build a workflow first. 3. **Giving the agent a broad standing identity.** One admin-level token lets a single injected instruction reach everything. Why it happens: broad access makes the demo work. Instead: grant each tool only the permissions its task needs, and require approval for writes, payments and external sends. 4. **Running the loop with no limits.** Without a step, time or cost ceiling, a confused agent keeps calling tools. Why it happens: limits feel like they block the goal. Instead: set a maximum number of iterations and a budget per run, and log every tool call. ## Sources - Anthropic, [Building effective agents](https://www.anthropic.com/engineering/building-effective-agents), December 19, 2024, checked October 2026. - Microsoft Learn, [AI agent shared responsibility model](https://learn.microsoft.com/en-us/azure/security/fundamentals/shared-responsibility-ai-agent), updated August 26, 2026, checked October 2026. ## In three sentences Agentic AI is software in which a language model picks its own next step and uses tools to act, looping until it finishes or a limit stops it. A chatbot only answers and a workflow follows steps fixed in advance, so use an agent only when the steps cannot be written down. Errors compound across steps (95% per step becomes 59.9% over ten), so the check, tight permissions, human approval for risky actions and hard limits decide whether an agent is safe to run. --- Canonical: