What Are AI Agents? A Plain Guide to the Reality and the Hype

An AI agent is a system in which a language model decides its own steps and uses tools, such as searching, calling software or editing files, to complete a task. That is different from a fixed workflow, where the steps are set in advance and the model only fills in specific parts. Many products sold as "agents" are really workflows, and for most marketing tasks that is the better choice.

"Agent" has become one of the most overused words in technology. Vendors apply it to chatbots, automations and genuinely autonomous systems alike. That makes it hard for a business to know what it is buying and what could go wrong. This guide sets out what the word means, how agents differ from chatbots and workflows, what they can reliably do in marketing today, where they fail, how to assess a vendor's claims, how to prepare your website for agents and how to start safely. For the automation side, see our guide to marketing automation with AI.

A clear definition

Anthropic's engineering guidance on building agents notes that "agent" can be defined in several ways. Some people use it for fully autonomous systems that operate independently over long periods using tools. Others use it for more prescriptive implementations that follow predefined workflows. Anthropic calls all these variations agentic systems and draws an architectural distinction:

• Workflows are systems where models and tools are orchestrated through predefined code paths.
• Agents are systems where models dynamically direct their own processes and tool usage, keeping control over how they accomplish tasks.

The same guidance recommends finding the simplest solution possible and only increasing complexity when needed, including possibly not building agentic systems at all, because agentic systems often trade latency and cost for better task performance. Source: Anthropic, Building effective agents (December 2024; the post notes that much of the tooling it describes has since changed).

Chatbot, workflow or agent?

	Chatbot or assistant	Workflow	Agent
Who decides the steps	The person, turn by turn	You, in advance	The model, as it goes
Uses tools	Sometimes	Yes, at fixed points	Yes, chosen dynamically
Predictability	High, since a person steers	High	Lower
Cost and speed	Low and fast	Moderate	Higher and slower
Best for	Questions, drafting, brainstorming	Repeatable processes	Open-ended tasks with unknown steps
Main risk	Wrong answers	Brittle rules	Unexpected actions, runaway cost

A useful test when you meet the word "agent" in a sales pitch: can you list the steps it will take in advance? If yes, it is a workflow, and that may be exactly what you want. If the vendor says it will "figure out what to do", ask what limits it has.

What agents can do in marketing today

These are tasks where agentic or AI-assisted approaches are commonly tried. We describe them as typical uses, not guarantees of quality, since results vary by tool and setup.

Task	How an agent might help	What a person must still do
Research	Gather sources, compare options, summarise findings with links	Check sources and conclusions
Reporting	Pull data from several tools, find changes and draft commentary	Confirm numbers and explanations
Content operations	Prepare briefs, check drafts against a style guide, flag missing facts	Approve, edit and own accuracy
Site audits	Crawl pages, spot issues and prioritise them	Judge impact and decide fixes
Campaign monitoring	Watch spend and performance, raise alerts and propose changes	Approve budget or targeting changes
Customer enquiries	Answer common questions, gather details and hand over	Handle complex or sensitive cases

Where agents fail

• Compounding errors. Each step can add a small mistake, and a long chain of steps can end up far from the goal.
• Overconfidence. Models can present wrong conclusions fluently. Google's guidance on generative AI content notes that models predict likely word sequences, not retrieve facts, so outputs can contain inaccuracies.
• Tool misuse. An agent with write access to your ad account or CMS can do real damage if it misreads a goal.
• Cost and latency. Many model calls per task add up.
• Poor handling of ambiguity. Agents struggle when instructions are vague or conflicting.
• Security. Agents that read untrusted web pages or emails can be tricked by hidden instructions in that content, often called prompt injection.
• Hard to audit. Without logs, you cannot tell why it did something.

How to assess an agent claim

Use these questions on any vendor, product demo or internal proposal.

1. What exactly will it do, step by step? If the answer is vague, ask for a real example run, with the logs.
2. What can it change? Read-only is safer than write access. Can it spend money, publish content or email customers?
3. What are the limits? Budgets, approval gates, allowed actions, time and cost caps.
4. How do I see what it did? Complete logs and a way to undo changes.
5. How is it tested? Ask for evidence on tasks like yours, with failure rates, not just a polished demo.
6. Where does my data go? Storage, retention and whether it trains other models.
7. What happens when it is unsure? It should stop and ask, not guess.
8. What does it cost per task at real volume?

The hype checklist

Claim	Reality check
"Fully autonomous marketing team"	Ask which decisions are made without approval and what the failure rate is
"Replaces your agency or analysts"	Tools take over tasks, not accountability. Someone must own results
"Guaranteed rankings or revenue"	No tool can promise this. Google advises caution about such claims
"Uses secret Google data"	Google says no third party has access to its internal ranking or AI systems
"Works out of the box"	Real use needs setup, data, rules and review
"Saves 80 percent of the time"	Ask how time was measured and whether review time was included

Agents visiting your website

Agents are not only something you use. They also visit websites on behalf of customers: comparing products, checking availability or filling in forms. Google's guide to generative AI features mentions agentic experiences and says browser agents may read a page through screenshots, the page structure and the accessibility tree. It points site owners to guidance on agent-friendly websites and notes that protocols for agent commerce are emerging. Practical steps for your site:

• Use clean, semantic HTML with proper headings, lists and tables.
• Label form fields clearly and make forms work without tricks.
• Make prices, availability and policies available as plain text on the page.
• Avoid blocking essential content behind pop-ups or actions an agent cannot perform.
• Keep accessibility strong. What helps screen readers also helps agents.
• Keep product and business data accurate, including feeds.

These steps are good practice for visitors anyway. Do not treat any of them as a guarantee that agents will choose you.

A safe way to start

1. Choose a read-only task. For example, summarising weekly analytics changes or researching competitors' pages.
2. Define success. What does a good output look like? How will you check it?
3. Limit access. Give it the minimum data and permissions it needs.
4. Keep humans in the approval path. Nothing is published, sent or spent without a person's sign-off at first.
5. Log everything. Keep records of each run.
6. Measure honestly. Compare time, errors and cost with the manual method, including review time.
7. Expand slowly. Add autonomy only where results are consistently reliable.

If a simple workflow does the job, use that. As Anthropic's guidance puts it, simple solutions are preferred and complexity is added only when needed.

Agents, SEO and AI search

Several SEO tools now describe themselves as agentic. We assess them in our guide to AI tools for SEO. Google also warns that no third-party tool has access to its internal ranking systems, and that you should evaluate advice from such tools against its official guidance. And if an AI agent writes your content, Google's rules on scaled content and accuracy still apply. See our article on AI-generated content and Google.

What is likely to change

Agents will keep improving, and more products will take actions on behalf of people. We would not predict specifics, since the field changes quickly. Some things are likely to stay true: tasks with clear steps will remain better suited to workflows, human accountability will remain necessary, and security and cost will remain real constraints. Judge each tool on evidence from tasks like yours, not on the label.

Common mistakes

• Buying an "agent" when a simple workflow would do.
• Giving write access too early.
• Skipping logs and approval gates.
• Trusting a demo instead of testing on your own tasks.
• Not counting review and maintenance time.
• Ignoring security, especially around untrusted content.
• Letting no one own the results.

A worked example: a reporting assistant

This is an invented example. A small agency wants help preparing monthly client reports. Here is how a careful team might use an agentic approach, step by step.

1. Define the task. "Each month, pull traffic and conversion data for a client, find the three biggest changes and draft a short commentary."
2. Decide workflow or agent. The steps are known in advance: fetch data, compare with last month, rank changes, draft text. That is a workflow with a language model at two steps, not a free-roaming agent.
3. Give read-only access. The system can read analytics and Search Console data, and cannot change anything.
4. Add checks. It must include the source and date for every number. A person verifies the numbers against the dashboards before anything is sent.
5. Define the failure route. If data is missing or an anomaly is larger than a set threshold, it flags it for a person instead of explaining it away.
6. Measure. The team records time per report before and after, including review time, and counts errors found in review.
7. Expand cautiously. After three months of reliable results, it adds a draft of recommendations, still with human sign-off.

Notice that the system never needed autonomy. The value came from structure, checks and clear limits, which is the point of Anthropic's advice to prefer the simplest solution that works.

An agent risk register you can copy

Risk	Example	Control
Wrong data or conclusions	A report states a traffic rise that was a tracking error	Verification step against source dashboards before sending
Unwanted actions	An agent pauses the wrong campaign	Read-only access, or approvals for every change
Cost runaway	A loop repeats thousands of model calls	Spend limits, call caps and alerts
Data leakage	Customer data pasted into an unvetted tool	Approved tools only, data minimisation and vendor terms review
Prompt injection	A web page contains hidden instructions	Treat external content as untrusted, limit tool permissions
Compliance breach	Auto-written ad claims a result it cannot support	Human review of claims, policy checklists
No audit trail	Nobody can tell why a change happened	Complete logs of inputs, outputs and actions
Over-reliance	Team stops checking because it usually works	Regular sampled reviews, even when results are good

Questions to ask your own team before building one

1. Could a simple workflow, or a single well-written prompt, do this task?
2. What is the cost of a mistake, and who would notice it?
3. What data does the system need, and are we allowed to share it with the tool?
4. What actions can it take, and what is the worst it could do?
5. How will we know it is working? What is the baseline?
6. Who owns it, and who fixes it when it breaks?
7. What happens when the vendor changes its model or pricing?

Glossary

Term	Plain meaning
Agentic system	Anthropic's umbrella term for both workflows and agents
Workflow	Models and tools orchestrated through predefined code paths
Agent	A system where the model directs its own process and tool use
Tool use	When a model calls software, such as search, a database or an API
Prompt injection	Hidden instructions in content that try to hijack a model
Human in the loop	A person reviews or approves actions before they take effect
Hallucination	A confident output that is not true, which Google notes can occur because models predict word sequences

What this means for small teams

Small teams benefit most from boring, reliable automation: scheduled reports, enquiry triage, content checklists and alerts. You do not need the most autonomous system to gain time. Spend your effort on clear process, accurate data and good review habits, and add autonomy only where evidence supports it. If a vendor cannot show a real example run on a task like yours, treat the promise as marketing.

Agent use cases ranked by risk, from safest to riskiest

Level	Use case	Why the risk is low or high	Controls
1. Read-only analysis	Summarise analytics, compare competitor pages, find broken links	Nothing changes if it is wrong. Cost of error is wasted time	Source citations, human review of conclusions
2. Drafting for review	Draft emails, briefs and reports that a person edits and sends	A person is the last gate	Style guide, fact check, approval before sending
3. Internal actions with undo	Tag support tickets, update CRM fields, create tasks	Mistakes are visible and reversible	Logs, undo, sampling
4. Customer-facing answers	Answer common questions in chat	Wrong answers reach customers	Approved knowledge only, clear handover to a person, labelled as an assistant
5. Spending money or publishing	Change ad budgets, publish pages, send campaigns	Direct financial and reputational impact	Approvals, caps, alerts, narrow permissions, staged rollout
6. Acting on behalf of customers	Book, buy or negotiate	Legal and financial commitments	Explicit authorisation, limits, receipts and dispute routes

Start at level 1 or 2. Move up only when evidence from supervised runs shows reliable results and you have tested the controls.

What vendors say, and what to ask

Vendor says	Ask
"Our agent runs your whole SEO."	List the specific tasks, what it can change and what a human must approve. Show a log of a real run on a site like mine
"It learns from your data."	What data, where is it stored, can I delete it and is it used to train models for other customers?
"It is trained on Google's ranking factors."	Google says no third party has access to its internal ranking systems. What is the actual source?
"It writes content at scale."	How do you prevent errors, duplication and scaled content abuse? Who is accountable for accuracy?
"Fully autonomous."	What happens when it is wrong, and who is alerted?
"Enterprise ready."	What security, access control and audit logs are included?

A checklist for agent-friendly websites

• Important facts, such as prices, availability, opening hours and policies, are in plain text on the page.
• Pages use semantic HTML: headings, lists and tables that match the content.
• Forms have visible labels and work with keyboard input.
• Buttons and links are real elements with clear text, not images or scripts that hide their purpose.
• Error messages are clear and tell people how to fix the problem.
• The site does not depend on hover, drag or timing to reveal essential information.
• Structured data matches visible content, where used.
• Product and business data are accurate in feeds and profiles.
• Security measures such as rate limits protect against abuse, without blocking legitimate crawlers you want.

Legal and ethical considerations

• Accountability. You remain responsible for what an agent does in your name, including messages, claims and purchases.
• Transparency. Tell people when they are dealing with an automated assistant, and offer a route to a person.
• Privacy. Agents that process personal data are subject to data protection law. Check lawful basis, vendor terms and retention.
• Advertising and consumer law. Automated ads and messages must meet the same standards as manual ones.
• Intellectual property. Check licences for data and content an agent uses and produces.
• Bias and fairness. Test outputs for unfair outcomes, particularly in targeting and eligibility decisions.

This is general information, not legal advice. Take advice for your situation.

A story of an agent pilot that stayed small on purpose

This is an invented composite. A Glasgow online retailer with a four-person marketing team was pitched an "autonomous marketing agent" that promised to manage its email, social posts and ad budgets. The head of marketing asked three questions: what exactly would it do, what could it change and how would they know if it went wrong? The vendor's answers were enthusiastic but vague, so she declined, and instead designed a small pilot herself.

The task she chose was monitoring. Each morning, a script would pull the previous day's ad spend, orders and website errors, compare them with the usual range for that weekday, and use a language model to write a short summary: anything unusual, the likely reasons and what to check. The system had read-only access, could not change any account and sent its summary to a shared channel. It also had to list the figures it used and where they came from.

In the first month, the summaries were helpful about half the time. On one morning, the system flagged a drop in orders and suggested checking the payment provider, which turned out to have had an outage. On another, it confidently explained a spike in spend as "increased competition", when the real cause was a budget change that a colleague had made and not recorded. The team noted that the model tended to invent plausible explanations for changes it could not actually see, so they edited the instructions: report the numbers and say "cause unknown" unless the data supports a reason, and list three things a person should check.

After three months, the monitoring agent was reliable enough that the team stopped manually reviewing the dashboards every morning, though they still looked at the summary and clicked through whenever it flagged something. The head of marketing measured the benefit: roughly 20 minutes saved per person per day, and two incidents caught earlier than they would have been. She also kept a log of errors, which were few but instructive, and she reviewed the access settings every quarter.

The company did not go on to hand over spending or publishing to an agent. It decided that for those tasks, the cost of an error was too high and the benefit too uncertain. The pilot showed that agentic approaches can be useful when they are given a narrow job, read-only access, clear instructions about uncertainty and a human to read the results. It also showed that the vendor's pitch, a fully autonomous marketing team, was solving a problem the retailer did not have.

Where we can help

We are a digital marketing agency in Manchester, UK and Mumbai, India. We help teams decide where automation, assistants or agents make sense, and where they do not. See our AI SEO services, or contact us for an honest second opinion on a vendor pitch.