Agentic AI vs AI Agents: Definitions, Differences and 10 Business Examples
Explainer · · Published October 2026 · 8 min read
By Ashwin Gopinath, CEO
TL;DR
- Agentic AI vs AI agents comes down to category versus instance. Agentic AI is the approach: AI systems that plan and take multi-step actions toward a goal. An AI agent is one such system, given a defined job, tools and limits.
- Every AI agent is agentic AI. Not all agentic AI is a single agent: it also covers agentic workflows, where a model works through steps a developer fixed in advance, and systems where several agents coordinate.
- For a business, the label matters less than four conditions: the agent has the right context, the right permissions, memory across runs, and a named person who approves high-impact actions.
- Sentra is the company memory and judgment that agents check with. It gives any agent, from ChatGPT and Claude to internal builds, what the company knows, under each source's permissions, and a named person approves anything Sentra raises.
See your agents work from memory that stays current.
Book a demoAgentic AI vs AI agents: the short definitions
The difference in agentic AI vs AI agents is scope. Agentic AI is the broad class of AI systems that pursue a goal by planning and taking actions over several steps, using tools and data rather than only producing a reply. AI agents are individual systems built that way: software that uses a model to decide which actions to take, with which tools, to complete a defined task.
So the comparison of AI agents vs agentic AI is a comparison of a thing and the category it belongs to. "Agentic" is an adjective that describes how much a system plans and acts on its own. "Agent" is a noun for one system that does it.
Anthropic draws the most useful line inside the category. It defines workflows as "systems where LLMs and tools are orchestrated through predefined code paths," and agents as "systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks." It groups both under the umbrella of agentic systems (anthropic.com).
That gives executives a practical vocabulary:
- Assistant or chatbot. Answers a question when asked. Takes no action.
- Agentic workflow. A model works through steps that were fixed in advance, such as read the invoice, compare it to the purchase order, draft an exception.
- AI agent. A model decides its own steps toward a goal, such as "resolve this ticket," choosing which tools to call and when to stop.
- Multi-agent system. Several agents split a larger goal and pass work between them.
All of the last three count as agentic AI.
The difference between agentic AI and AI agents, in one table
| Dimension | Agentic AI | AI agents |
|---|---|---|
| What the term names | A category and design approach | A specific system doing a specific job |
| Scope | Can span many tasks, systems and agents | Usually one task or one domain |
| Who decides the steps | Varies: fixed code paths in workflows, the model in agents | The model, within the limits it was given |
| Typical form | Agentic workflows, single agents, multi-agent systems | A support agent, an invoice agent, a coding agent |
| What a buyer evaluates | Strategy, governance, architecture | A product, its accuracy, its permissions |
| Main risk | Agents spread without governance or shared context | One agent acting on wrong or stale information |
| Question to ask | Where should AI act on its own in our company? | Should this agent be allowed to do this task? |
What is agentic AI, and why the term took off
Agentic AI became the industry's term for AI that moves from answering to doing. Gartner named agentic AI its top strategic technology trend for 2025 and predicted that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024 (gartner.com).
The term is also overused. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls. It describes "agent washing," the rebranding of existing assistants, RPA and chatbots without substantial agentic capability, and estimates only about 130 of the thousands of agentic AI vendors are real (gartner.com).
A quick test for agent washing: ask the vendor what the system does when the first approach fails. A scripted bot stops or escalates. An agent tries another route within its limits. Both can be useful, but they are different purchases.
Agentic workflows vs AI agents: which to use
Agentic workflows are the right default for most business processes. They are predictable, easier to test and easier to audit, because the steps are known. Anthropic recommends "finding the simplest solution possible, and only increasing complexity when needed" (anthropic.com).
Use an agentic workflow when:
- The steps are the same every time, such as matching invoices or routing tickets.
- An auditor needs to see exactly what happened.
- A wrong step is expensive.
Use an AI agent when:
- The path to the goal changes from case to case, such as investigating why a metric moved.
- The agent needs to search, read and decide what to look at next.
- A person reviews the result before it takes effect.
AI agent examples: 10 business uses by function
These ten AI agent examples show where agentic AI is used in companies today. Each names the type of system and the point where a person approves.
| Function | Example | Type | Human approves |
|---|---|---|---|
| Finance | Match invoices to purchase orders and contracts, flag exceptions | Agentic workflow | Payment of any flagged invoice |
| Finance | Explain month-end budget vs actual gaps from ledger and email | AI agent | Commentary before it goes to leadership |
| Sales | Spot renewal risk from usage, support tickets and email tone | AI agent | Outreach to the customer |
| Customer support | Triage tickets, draft replies, resolve common cases | Agent with workflow guardrails | Refunds and policy exceptions |
| HR | Run onboarding steps: accounts, equipment, training schedule | Agentic workflow | Access to sensitive systems |
| IT | Handle access requests against role policy | Agentic workflow | Requests outside policy |
| Operations | Find stockouts where reported stock and shelf stock disagree | AI agent | Replenishment orders |
| Procurement | Catch spend outside contracted suppliers | Agentic workflow | Supplier changes |
| Legal | Track contract obligations and upcoming deadlines | AI agent | Any notice sent to a counterparty |
| Engineering | Write and test code changes from an issue | AI agent | Merge to production |
For the wider picture of AI agents for business, see our business guide.
What AI agents need to work inside a company
The model is rarely the limiting factor. Agents fail at work for the same reasons a new hire would: they lack context, they have the wrong access, they forget what happened last week, or nobody checks their work. Gartner lists agentic AI relying on low-quality data and making decisions that are not trustworthy among the risks it flagged alongside its 2025 trend (gartner.com).
Context
An agent needs to know how the company actually runs, not only what is written in documents. The invoice is in the ERP. The reason for the discount is in a sales call. The decision to change supplier is in a chat thread. An agent that sees only one of those acts on half the story. Our explainer on context engineering covers how that context is assembled.
Permissions
An agent should see and do only what the person it acts for could. OWASP lists Excessive Agency as a top risk for LLM applications, caused by too much functionality, too many permissions or too much autonomy (genai.owasp.org). Our guide to AI agent permissions goes through the controls.
Memory
Most agents start every run from zero. They re-read the same files, repeat questions already answered, and cannot tell that a plan from last quarter has been replaced. Memory across runs, and across agents, is what lets one agent's finding inform the next. See why AI agents forget.
Approvals
High-impact actions need a named person to approve them. OWASP recommends human-in-the-loop control "to require a human to approve high-impact actions before they are taken" (genai.owasp.org). Decide in advance which actions an agent can take on its own and which wait for sign-off.
Where Sentra fits
Sentra is not an agent builder. It is the company memory and judgment that agents check with before they act. Build or buy agents however you like, in ChatGPT, Claude, Copilot, Gemini or your own code. Sentra supplies the context and memory from the section above, and enforces the permissions.
It connects email, chat, meetings, documents, ERP, CRM, HRIS and BI through 200+ integrations, and works out what is true now, why, and who is working on what. It knows when a fact became true and when it stopped being true, so an agent does not act on a superseded price, plan or owner. Agents reach it through REST and MCP, so every agent in the company draws on the same memory rather than each keeping its own partial copy.
Answers and actions respect each source's existing permissions, checked at query time, and a failed check counts as no access. Sentra also acts first: it finds what is going wrong, works out who should act, and tells them. A named person approves anything Sentra raises before it reaches anyone else. Sentra holds SOC 2 Type II and ISO 27001 and runs in the cloud, in an isolated VPC, or air-gapped on premises.
The memory also lowers agent cost. On Terminal-Bench 2.1, agents using Sentra scored 88.31% against an 83.37% baseline, with 72.6% lower model cost and 41.2% fewer tokens.
To see how your agents would use company memory, book a demo at /demo.
How to start with agentic AI
1. Pick one process that crosses systems. The value of agentic AI shows up where a person today has to join an ERP record with an email thread.
2. Start with an agentic workflow, not a free agent. Fix the steps, measure the result, then loosen the steps where cases vary.
3. Write the approval rule before launch. List which actions the agent may take alone and which wait for a named person.
4. Give it the same context a person would have. Connect the systems the process depends on, with source permissions intact.
5. Measure against a baseline. Hours, errors and cycle time before and after. Our AI ROI guide has a worked example.
For enterprise rollout patterns, see enterprise AI agents.
FAQ
What is the difference between agentic AI and AI agents?
Agentic AI is the category of AI that plans and takes multi-step actions toward a goal. An AI agent is one system in that category, built for a specific task with specific tools and limits.
Is ChatGPT an AI agent or agentic AI?
A chat assistant answering questions is neither. When an assistant is set up to take actions with tools, such as browsing, running code or calling business systems, toward a goal, that setup is an AI agent and part of agentic AI.
What are agentic workflows?
Agentic workflows are processes where a model and tools follow steps fixed in advance by a developer. Anthropic calls these workflows, as distinct from agents, which choose their own steps (anthropic.com).
What are some examples of AI agents in business?
Invoice matching in finance, renewal risk detection in sales, ticket resolution in support, onboarding in HR, access requests in IT, stockout detection in operations and coding agents in engineering. The table above lists ten, with the approval point for each.
Are AI agents safe to use in a company?
They can be, with least-privilege permissions, actions that respect source access controls, and a named person who approves high-impact actions. OWASP's Excessive Agency guidance is a good checklist (genai.owasp.org).
Why do agentic AI projects fail?
Gartner cites escalating costs, unclear business value and inadequate risk controls (gartner.com). Missing context and missing approvals are the two operational gaps to check first.