AI Agents Aren't Digital Employees. Here's What They Actually Are.

The term 'agent' gets thrown around loosely. Here's how agents differ from related concepts:

Scripts execute a fixed sequence of steps. Same input, same output, every time. No interpretation.

Workflows follow predetermined paths with branching logic. If X happens, do Y. Deterministic and predictable.

Chatbots respond to queries but don't take action. They answer questions; they don't complete tasks.

Agents choose their next steps dynamically based on context and goals. They can interpret unstructured information and adapt their approach.

The key distinction: traditional automation follows rigid rules; agents use probabilistic reasoning. This makes them flexible — but also less predictable.

This is where things usually break. Many vendors are rebranding basic chatbots as 'intelligent agents' — what industry observers call 'agent washing.' A researcher who has studied multi-agent systems for three decades recently said he's 'amused' by the sudden marketing popularity of the term.

Traditional rule-based automation isn't obsolete. In many cases, it's still the better choice.

Use traditional automation when:

The process is stable and well-defined. The inputs are structured. You need auditability and predictable outputs. The stakes are high enough that you can't tolerate variability.

Use agent-based automation when:

Inputs are unstructured — like free-text emails or varied customer requests. The task requires interpretation rather than just execution. Traditional automation was impossible because the rules couldn't be written in advance.

The key insight: agents add value where traditional automation was impossible. But don't replace a working deterministic workflow with an agent just because it's newer.

Automating a bad process makes it fail faster. The same applies to replacing a reliable process with something fancier.

This is where the 'autonomous AI employee' narrative falls apart.

AI agents cannot be trusted to operate independently in high-stakes environments — finance, healthcare, legal, or anywhere errors have real consequences. They lack the contextual understanding humans have. They can't be held accountable. They don't understand consequences.

In practice, agents should:

Prepare, research, draft, and triage. Humans approve, override, and make final calls. Escalation triggers should be explicit and reliable.

When humans are removed too early, predictable failure modes emerge:

Cascading errors. One hallucination compounds into many bad outputs. The agent proceeds confidently, making things worse with each step.

Silent failure. The agent doesn't know what it doesn't know. It continues confidently even when wrong, without flagging uncertainty.

Accountability gaps. When something goes wrong, who is responsible? The software can't be. That leaves whoever removed the oversight.

Humans decide. Machines execute. That isn't a constraint — it's the design.

The grounded use cases aren't flashy. They're about handoffs, data gathering, triage, preparation, and follow-ups — not final decisions.

Operations: Inventory monitoring and alerts. Flagging when maintenance is likely needed. Surfacing quality control issues for human review.

Sales admin: Lead scoring and prioritisation. CRM data enrichment. Meeting preparation — gathering context before calls.

Customer handling: First-line triage — understanding what the request is about and routing it correctly. FAQ responses for common questions. Drafting replies for human review.

Finance support: Invoice matching. Expense categorisation. Drafting reports for review.

Coordination: Meeting scheduling. Document routing. Aggregating status updates from multiple sources.

Notice what these have in common: they're preparation and handoff tasks, not decision-making tasks. The agent gathers information, organises it, and presents it. A person decides what to do with it.

Use cases that look impressive but typically fail: 'fully autonomous' customer service without escalation paths. AI making pricing or approval decisions without oversight. 'Set and forget' agents in dynamic environments.

When a vendor describes their agent as 'fully autonomous,' treat it as a warning sign, not a selling point.

Agents require logging, audit trails, and fallbacks because they're less predictable than traditional automation. According to enterprise audit research, 62% of audits are failing due to inconsistent trails — and that's before adding probabilistic AI into the mix.

The risks specific to agents:

Error amplification. One bad input leads to many bad outputs. Traditional automation hits a wall; agents dig a hole.

Over-confidence. Language models don't know what they don't know. They present uncertain information with the same confidence as certain information.

Context drift. The agent optimises for a measurable target while missing the actual goal. It does exactly what you asked — which turns out not to be what you meant.

'Fully autonomous' typically means: no oversight, no correction, no accountability, no fallback. That's not a feature. That's a risk.

If it depends on heroics, it's fragile. The same applies to systems that depend on AI never being wrong.

Part of the confusion around agents comes from language that implies capabilities that don't exist.

Avoid: 'AI employee' — implies accountability that doesn't exist. 'Autonomous' — implies no oversight needed. 'Thinks' or 'decides' — implies consciousness and responsibility. 'Intelligent' — overstates capability.

Prefer: 'Automated assistant' instead of 'AI employee.' 'Supervised automation' instead of 'autonomous agent.' 'Processes' or 'analyses' instead of 'thinks.' 'Recommends' instead of 'decides.'

This isn't pedantry. Language shapes expectations. When you describe an agent as 'autonomous,' people assume it can be left alone. When you say it 'decides,' people assume it's accountable for decisions. Neither is true — and the mismatch causes problems.

If you can't explain it to the person doing the work, it isn't ready.