One vendor says its product is "agentic." Another says it sells a team of AI agents. Neither claim tells you what happens when a customer writes in asking where their order is. You'll get further by deciding which decisions you're willing to hand over and which actions still need a person's sign-off.
A working definition: an AI agent is software that uses a model and tools to carry out a task. "Agentic" describes a capability, the ability to choose the next step and act toward a goal without someone directing each move. One agent can be agentic. So can a group of them.
Below, we run one customer request through a rule, an assistant, a single agent and a team of agents, then work out how much authority to delegate and what your team should review.
What the label leaves out
Vendors draw the line in different places. Anthropic separates predefined workflows from agents that direct their own tool use. Google Cloud describes agents by their capabilities and sorts them into several categories, including single-agent and multi-agent setups. Ask any vendor to explain its definition in terms of your process.
A serious proposal should answer four questions, using a request from your business:
- 01What starts the work?
- 02Which decisions has your team fixed in advance?
- 03Which choices does the model make on its own?
- 04What makes it stop?
Compare current models and agent products
A model and an agent product are different layers. The model does the reasoning. The product connects it to tools, memory and your apps, and decides how the work gets coordinated.
The details below were checked on November 13, 2026. Use them to tell the layers apart, and judge performance on your own cases before choosing anything.
- 01Grok: Grok 4.7 is a model release. Grok Bot 1.0 is the product you delegate ongoing work to. Team Bots can work inside Slack or Microsoft Teams and bring another Bot into a task, which gives you a multi-agent option, though a single Bot can do agentic work on its own. Grok 4.7 and Grok Bot 1.0.
- 02OpenAI dots: A dot takes on an ongoing responsibility that carries across chats. Dots combine models, connected apps, a cloud computer and continuing context. OpenAI added GPT-6.1 Terra to the available models in its November update. OpenAI's product announcement.
- 03Meta Muse: Meta's personal agent received specialized models trained using Muse Wonder in its November 6 update. Meta reports better retention of relevant context and better persistence toward a goal when problems come up. You talk to it in a conversation while it may work through several steps behind the scenes. Meta's November update.
- 04Claude Duet: Anthropic's proactive agent runs on Opus 5.5 with connected apps and its own cloud computer. It's still a US research preview with restricted access. Anthropic designed it to ask before consequential actions such as sending messages or making payments. Product details and access requirements.
- 05Hermes Agent: Nous Research's open-source agent supports different model providers, persistent memory, scheduled tasks and delegation to subagents. You can switch models and keep the same agent software. The stable release we checked was v0.23.1. Official documentation and v0.23.1 release.
Whichever you look at, find out which records it can read, which changes it can make and where it pauses for review. Then ask the vendor to show you how unfinished work gets recovered after a failure.
The same customer, handled four ways
Here's a fictional retail request we'll use for the rest of the article: "My order was supposed to arrive today. Can you check it and send it to my office instead?"
The customer wants two things, a status update and a change to the shipment. The second is where the risk sits. Whatever design you choose, confirm that this person has the authority to redirect the delivery. Knowing the order number doesn't prove it.
- 01A conventional workflow follows a fixed path. It authenticates the customer, retrieves the order and routes any address change to a person. If you can write down every permitted step and exception in advance, this is a strong choice and often the cheapest.
- 02An AI assistant helps an employee read the message and draft a reply. The employee still decides what to look up, in what order, and what to do about it.
- 03A single tool-using agent decides for itself whether it needs the order record, the carrier update or the address-change policy. It might produce a status reply and an escalation in one pass. You can give it those tools and still withhold permission to touch the shipment.
- 04A coordinated system splits the work, for example one agent researching the order while another checks policy, then merges what they find. That split needs a concrete reason, such as different access permissions or a large amount of independent work. Sounding more advanced isn't a reason.
For many retailers, a good result looks like this: a reply that states the latest verified tracking event, with the address change routed to a reviewer. If that's the outcome you want, write it down as the expected result.
Delegate one decision at a time
Start by listing the decisions in the process. For the retailer, that means matching the customer, retrieving tracking and checking the address-change policy. Some of those steps suit exact rules. Others involve messy human language that a model handles better. You don't have to pick a single approach for the whole job.
- 01Rules or an existing integration fit structured input and a predictable action, like sending a low-stock alert when inventory hits a threshold your team approved.
- 02An AI-assisted step fits varied language, classification or drafting inside a fixed sequence. A person or an established workflow still decides what happens next.
- 03One agent makes sense when the next lookup depends on what the previous one found. Define the choices it may make and the point where it has to stop or escalate.
- 04Several agents make sense for distinct permissions, independent checks, specialist analysis or work that can run in parallel. Someone still has to check the combined result, and the coordination costs something.
Microsoft's architecture guidance recommends starting with the least complexity that meets the requirement. If you haven't picked a task yet, start with our first-automation guide.
Let it investigate without letting it act
Treat reading a delivery update, drafting an explanation and changing an address as three distinct capabilities, each granted on its own.
For the retailer, the agent retrieves the relevant records and prepares an escalation. A person approves any address change through the normal customer-verification process. Once a change is approved, keep the carrier's confirmation so the reviewer can see it was actually accepted.
Build the review around evidence a person can check quickly: the matched customer and order, the latest information, the requested change and anything still unresolved. If the reviewer has to redo the lookups before trusting the proposal, the agent saved less time than it seems, so count that time in your evaluation. Our guide to auditable workflows covers how to design this step.
Price the whole job
The number that matters is what it costs to resolve a request, measured from the moment it arrives to a checked outcome. That includes tools and software, repeated attempts, review time and maintenance, plus whatever manual work someone does when the system can't finish.
Parallel agents add a cost that's easy to overlook: their outputs have to be reconciled. One agent may pull a newer carrier update than another, or finish while the other fails. Ask the vendor how its software notices those conflicts and decides which result is current.
Across the designs you're considering, compare total handling time, incorrect customer matches, unsupported statements and review effort. Keep the components that improve those numbers enough to pay for themselves. Our automation ROI guide can help you estimate the cost and the capacity your team would get back.
Test the hard cases before you expand
Ask the vendor to run a routine request and a difficult one from start to finish, using cases from your own process with personal details removed or appropriate access in place.
For the retailer, include the cases below. Decide what a pass looks like before you run them, and keep the failures so you can retest each revision against the same problems.
- 01Missing information: the order matches, but the carrier has no current estimate. A pass states the known facts and doesn't invent an arrival date.
- 02Ambiguous identity: two orders could match the message. A pass asks for what it needs to resolve the match without revealing the other customer's details.
- 03A forbidden action: the customer asks for a refund or address change beyond the agent's permission. A pass hands the case to the responsible person and leaves the record unchanged.
- 04An interruption: a lookup fails, or an employee picks the case up later. A pass shows the unfinished step and doesn't repeat an action that already succeeded.
If your team can't keep up with reviews, fix that before you raise the volume. Run the fixed workflow and the agent on the same cases, and weigh both error rates and cost when you decide.
Bring a defined request to an assessment
Bring your process, the systems it touches and a handful of representative cases to an automation assessment. We'll work through which steps need AI, which can run on rules and which actions a person should approve.
What to keep
- 01"AI agent" and "agentic AI" overlap. Define the decisions and actions you want to delegate instead of debating the label.
- 02A single agent can be agentic. Use several agents only for a specific division of work or permissions.
- 03Permission to investigate is separate from authority to change records or make commitments.
- 04Compare the cost of a completed job, including review, exceptions and upkeep.
- 05Test representative work and failure cases before you expand an agent's authority.
Frequently asked
What is the difference between AI agents and agentic AI?
An AI agent is software that uses a model and tools to carry out tasks. Agentic AI describes the ability to decide on next steps and act toward a goal. Vendors use the terms in overlapping ways, so compare the decisions, access and actions a proposed system actually supports.
Does agentic AI require multiple agents?
No. A single agent can make intermediate decisions and use tools toward a goal. Multiple agents are worth it when you can justify separate responsibilities and the extra work of coordinating them.
Can an AI agent work without permission to send messages or change records?
Yes. An agent can investigate, retrieve information and prepare a proposal while a person keeps the authority to send messages or change records. Define and enforce those permissions as separate capabilities.
Sources and further reading
- 01Building effective agents (Anthropic)
- 02What are AI agents? (Google Cloud)
- 03AI agent orchestration patterns (Microsoft Learn)
- 04Warehouse photograph by Russ Murray (Unsplash)
- 05Grok 4.7 model announcement (Grok)
- 06Grok Bot 1.0 announcement (Grok)
- 07Introducing dots, updated November 3, 2026 (OpenAI)
- 08Specialized models for Muse and Meta AI (Meta)
- 09Claude Duet product details (Anthropic)
- 10Claude Duet access requirements (Anthropic)
- 11Hermes Agent documentation (Nous Research)
- 12Hermes Agent v0.23.1 release (Nous Research)
Interzekt Editorial Team
Editorial Team at Interzekt
The Interzekt Editorial Team publishes practical reporting and guidance on AI systems, automation, and the operating decisions behind useful technology.