The trench coat looks convincing. The credentials are polished. The introduction is impeccable. Then you ask the agent to investigate something unexpected, and it reads you the script.
Call it agent washing: dressing up familiar automation in the language of autonomy. A sequence becomes a digital worker. A prompt becomes a colleague. The product gets a new identity; the customer still does the difficult thinking between steps.
Look past the badge.
There is no single definition of an AI agent. But a useful distinction exists. Anthropic describes workflows as following predefined paths, while agents use models to direct their process and tool use. Both can be valuable. The question is whether the description matches the behaviour.
A well-designed workflow can be exactly what a business needs. The trouble starts when predictable automation is sold with the promise of judgement, adaptation and independent follow-through. Buyers inherit expectations the product cannot meet.
The badge says agent. The work should tell you the rest.
Judge an agent by how it handles the work when the answer is uncertain.
Give it a case worth investigating.
A tidy demonstration rarely reveals much. Try a messy account instead: contradictory research, an outdated executive profile, an apparent buying signal with no obvious business need.
Ask what the system can establish, what it needs to investigate and where it should stop. Can it change its approach when new evidence appears? Can it show the sources behind a recommendation? Does it recognise the difference between a plausible opportunity and a verified one?
These are more revealing questions than how many agents appear on a product slide. A system that knows when to ask for human judgement may be more useful than one that confidently completes the wrong task.
Put the outcome under examination.
In revenue work, an impressive answer is only part of the job. Someone still needs to understand the account, identify the people involved and decide whether there is a conversation worth having. More autonomous activity creates little value if those decisions remain weak.
That is the standard we believe Agentive should be held to. Scout surfaces potential opportunities for investigation. Account and buying-group context supports preparation. Sellers review the evidence and decide the next move. The value must show up in the quality of that work.
Ask every vendor, including us, to demonstrate the limits as clearly as the capabilities. What is automated? What adapts? What requires your approval? What useful work is actually completed?
A good agent should stand up to a little investigation.
