Editorial Standards & Methodology
How we check, score and correct.
AI agents are moving from answering questions to actually doing things.
They can browse, write, research, use software, move information between systems, and sometimes take actions without someone watching every step.
That makes the way we review them matter.
At Agent Intellect, we don’t want to call an agent “powerful” because a company says it is. We want to know what it can actually do, how much work it can handle, where a person still needs to step in, and what happens when it gets something wrong.
This page explains how we make those judgments.
Independence
Agent Intellect is editorially independent from the companies we cover.
We don’t accept payment, free access, partnership terms, or other incentives in exchange for a better score, ranking, review, or mention.
Advertising is separate from editorial coverage.
A company can advertise with us and still receive a critical review. A strong product can still receive a lower score if our testing doesn’t support a higher one.
Commercial relationships do not decide our editorial conclusions.
How we label evidence
We don’t want readers to confuse something we tested with something a company told us.
So we use four evidence labels throughout our reviews.
We Checked
We used the product ourselves for a real task and saw what happened.
This is hands-on evidence. It isn’t a formal benchmark conducted over weeks, and it doesn’t mean we’ve tested every possible use case. It means an editor actually used the product and can stand behind what they observed.
Documentation Verified
The information is supported by the company’s documentation, help center, technical documentation, pricing page, or other official material.
This tells you what the product officially supports. It doesn’t necessarily mean we personally tested that particular feature.
Vendor Reported
The claim came from the company and we haven’t independently confirmed it.
We may still include vendor-reported information when it’s useful, particularly for things like company-reported performance figures, customer numbers, benchmarks, or upcoming capabilities.
The important part is that we tell you where the claim came from.
Editorial Assessment
This is our judgment.
It covers things we evaluate rather than simply verify, such as how useful a workflow feels, whether an interface gets in the way, how much supervision an agent needs, or whether a product seems worth using for a particular job.
An editorial assessment is an opinion based on the evidence. We don’t pretend it is a laboratory measurement when it isn’t.
How We Score AI Agents
Our scoring framework focuses on four things:
Autonomy. Workflow Depth. Human Control. Risk.
We chose these because a long feature list doesn’t tell you much about what it actually feels like to hand work over to an AI agent.
Autonomy
How much can the agent do on its own?
We look at whether it can plan, make decisions, use tools, move between steps, deal with problems, and continue without constant instruction.
More autonomy isn’t automatically better. An agent that can act without asking you is useful—until it takes the wrong action.
Workflow Depth
How much of the actual job can it handle?
There’s a big difference between completing one small action and carrying a task through several stages.
We look at how far the product can take a real workflow rather than simply counting features on its website.
Human Control
How easy is it to stay in charge?
We look for approvals, pause and stop functions, permissions, confirmations, editable actions, audit trails, and ways to override the agent.
The more an agent can do, the more important these controls become.
Risk
What does a mistake cost?
If an agent writes a bad headline, you can fix it.
If it sends the wrong customer an email, changes a record, deletes something important, or takes an action that can’t easily be undone, the situation is different.
Our risk assessment considers potential damage, how easy a mistake is to spot, and whether the action can be reversed.
A Score Is a Snapshot
Our scores describe the product as we tested it, not the company forever.
AI products change quickly.
A model gets upgraded. A feature moves behind a higher-priced plan. An integration breaks or gets added. An agent becomes more autonomous. A company changes how data is handled.
That’s why our reviews are tied to the product version and testing date where practical.
When a meaningful change happens, we may revisit the product and change the score.
If the product changes, our assessment can change too.
How We Test
Whenever possible, we use the products ourselves.
We prefer task-based testing over feature demonstrations. We give an agent tasks that match its intended use and observe what happens throughout the workflow—not just whether the final result looks good.
Depending on the product, we may assess:
- Task completion
- Number of steps handled
- Planning and decision-making
- Recovery from mistakes
- Human intervention required
- Tool and integration reliability
- Controls and permissions
- Response speed
- Pricing-plan limitations
- Data handling
- Whether the product’s capabilities match its marketing
A hands-on test is evidence, not a guarantee of future performance.
An agent may behave differently with another model, account, integration, configuration, or task. Our testing shows what happened under the conditions we tested.
Where our testing is limited, we say so.
We Don't Score Feature Lists
A product having a feature doesn’t tell us whether that feature is useful.
An agent might advertise browser control, computer use, CRM integrations, multi-agent workflows, or autonomous research.
The question we care about is what happens when you actually use those capabilities.
Can it finish the task?
Does it need constant intervention?
Does it know when it is stuck?
Can you see what it is doing?
Can you stop it?
And if it makes a mistake, can you undo it?
Those questions matter more to us than the number of boxes a product can tick.
Fact-Checking and Vendor Claims
We check important claims against the strongest sources we can find.
Depending on the subject, those may include:
- Official product documentation
- Technical documentation
- Pricing pages
- Company announcements
- Academic research
- Government sources
- Public datasets
- Financial disclosures
- Independent testing
- Expert commentary
- Our own product testing
Companies know their products better than anyone—but they’re also selling them.
If a company says its agent has a particular accuracy rate, completed a benchmark, supports a capability, or will launch a feature soon, we don’t automatically treat that statement as independently fact.
If we can’t verify it ourselves, it stays Vendor Reported.
That doesn’t mean the claim is false. It means we’re being clear about what we know and how we know it.
AI-Assisted Writing
We use AI tools in parts of our own editorial workflow.
That can include research assistance, summarization, transcription, drafting, editing, and organizing information.
But AI doesn’t get the final say.
Every published piece is reviewed, fact-checked, and edited by a human editor.
Our analysis, opinions, scoring decisions, and final editorial judgments are made by people.
Using AI to help produce an article doesn’t mean we outsource responsibility for that article to AI.
Reviews, Analysis and Opinion
Not everything we publish is a product test.
Agent Intellect also covers industry developments, automation, business adoption, product strategy, and the changing relationship between people and software.
Some pieces are reporting. Others are analysis or opinion.
We try to make that distinction clear rather than presenting an editorial judgment as an established fact.
When we make predictions about where AI agents are heading, those are predictions—not promises.
Scores Are a Snapshot
Our scores describe the product as we tested it, not the company forever.
AI products change quickly.
Models get upgraded. Features move between plans. Integrations change. Privacy policies change. Agents become more or less autonomous.
That’s why our reviews are tied to the product version and testing date where practical.
When a meaningful change happens, we may revisit the product and change the score.
If the product changes, our assessment can change too.
Corrections
We get things wrong sometimes.
When we do, we fix the original article and explain what changed and when.
Small things, such as spelling mistakes or minor formatting issues, may be corrected without a formal notice.
If we’ve published something materially wrong, we don’t quietly rewrite the history of the article.
If the error affects a product’s score, recommendation, or conclusion, we add a visible correction note and explain the change.
If our evidence changes, you should be able to see that it changed.
Conflicts of Interest
Our writers and editors should disclose relevant conflicts that could affect their coverage.
Editorial decisions should not be influenced by financial relationships, partnerships, affiliate arrangements, investments, free products, or vendor incentives.
If there is a relationship that a reader should know about, we’ll disclose it.
Keeping Reviews Up to Date
An AI review can age quickly.
Pricing changes. Models change. Features disappear. New ones arrive. A product that needed constant supervision six months ago may now handle much more on its own.
We periodically revisit products where meaningful changes are likely.
When we do, we may reassess:
- Autonomy
- Workflow depth
- Human control
- Risk
- Features
- Pricing
- Integrations
- Privacy
- Overall usefulness
A newer test can lead to a different score.
That’s not inconsistency.
That’s what should happen when the product itself has changed.
The Standard We Try to Follow
We don’t expect readers to simply trust us.
We’d rather show you why we reached a conclusion.
- If we tested it, we’ll say so.
- If the documentation supports it, we’ll say so.
- If the company says it but we haven’t verified it, we’ll label it.
- If it’s our judgment, we’ll call it an assessment.
- If we get something important wrong, we’ll correct it.
AI agents are becoming capable of doing real work.
That means reviews need to look beyond demos and feature lists.
We want to know what these systems actually do when they’re given a job—and what happens when they don’t do it quite right.
That’s the standard we use at Agent Intellect.
Contact the Editorial Team
If you spot something that looks inaccurate, outdated, or misleading, please let us know.
You can contact our editorial team about:
- Factual errors
- Outdated product information
- Pricing changes
- Incorrect scores
- Product updates
- Evidence that challenges something we’ve published
- Questions about our methodology
We’ll review credible corrections and update our coverage when the evidence supports doing so.