What Are AI Agents and How Are They Different to Train?
AI agents are the most discussed development in applied AI right now. Every major AI company has announced an agentic product. Every major enterprise software vendor is building agent capabilities into their platforms. The market has moved on from the question of whether agents will matter to how to make them reliable.
What is less discussed is what it takes to train AI agents well, how that differs from training standard language models, and why the human expertise required for agent evaluation is more demanding than what general AI training involves.
What an AI agent actually is
A language model responds to a single input and produces a single output. An AI agent takes an instruction and pursues a goal across multiple steps, taking actions along the way.
Those actions might include: searching the web, writing and executing code, reading and modifying files, sending emails, booking calendar entries, querying databases, or calling external APIs. An agent does not just answer a question. It does things in the world on behalf of a user.
This changes the stakes considerably. A language model that gives a wrong answer has produced a bad output. An AI agent that takes a wrong action may have sent a misleading email, deleted a file, made a booking, or submitted a form. The consequences of errors are no longer confined to text. They extend into real-world systems.
How agent training differs from standard AI training
Training a standard language model to produce better responses involves human evaluators assessing outputs and providing preference feedback. The unit of evaluation is a single response to a single prompt.
Training an AI agent involves evaluating something more complex: a sequence of actions taken toward a goal, where the quality of the overall sequence matters, individual steps may be locally reasonable but globally wrong, and the agent's decisions at early steps affect what options are available later.
Multi-step evaluation
When a human evaluator assesses an agent's behaviour on a task, they are not just asking "was this final output correct?" They are asking: did the agent plan appropriately? Did it take the right actions in the right order? Did it recover gracefully when it encountered unexpected obstacles? Did it stop and seek clarification when it should have, rather than proceeding with insufficient information?
These judgments are considerably more demanding than single-response evaluation. They require the evaluator to follow a sequence of actions, understand the causal relationships between them, and assess the quality of the overall approach rather than just the final result.
Tool use and action correctness
An agent that uses tools needs evaluators who can assess whether the tools were used correctly. An agent that writes and executes code needs evaluators who can read that code and determine whether it was correct, efficient, and safe. An agent that queries a database needs evaluators who understand the query language and the data schema. An agent that drafts a legal document as part of a workflow needs evaluators who can assess the legal quality of what was drafted.
Tool use evaluation is inherently domain-specific. There is no general evaluator for an agent that assists with structural engineering calculations, or one that assists with pharmaceutical regulatory submissions. The evaluator needs to understand both what the agent was trying to do and whether it did it correctly using the tools available.
Safety and irreversibility
Agent actions can be irreversible in ways that language model outputs are not. An agent that deletes the wrong files, sends an email to the wrong recipient, or submits a form with incorrect data has caused harm that a corrected output cannot undo.
Safety evaluation for agents therefore focuses heavily on: does the agent take appropriately cautious actions in high-stakes situations? Does it confirm before taking irreversible actions? Does it correctly identify when a task is outside its reliable competence and stop rather than proceeding?
These are nuanced safety judgments that require evaluators with both domain expertise and an understanding of what appropriate caution looks like in the specific context the agent is operating in.
Failure mode variety
Standard language model failures are mostly variations on a few themes: factual errors, hallucinations, safety boundary failures, unhelpful responses. Agent failures are more varied and context-dependent. An agent might fail because it misunderstood the goal, chose the wrong tool, used the right tool incorrectly, got stuck in a loop, or made a reasonable individual decision at each step but arrived at a collectively wrong outcome.
Identifying and documenting agent failure modes requires evaluators who can think through multi-step sequences and diagnose where in the chain things went wrong, not just that the end result was wrong.
Who is needed for agent evaluation
The expertise requirements for agent evaluation are higher than for standard AI evaluation and more diverse.
Software engineers are needed to evaluate agents that write, execute, and debug code. The agent's code outputs need to be assessed for correctness, security, efficiency, and production readiness.
Domain specialists are needed for agents operating in specific professional contexts. An agent assisting with financial analysis, legal research, or clinical documentation needs evaluators with the relevant professional knowledge.
Process specialists are needed to evaluate whether an agent's multi-step approach is sound, not just whether individual actions are correct. Someone who understands how a specific type of work is actually done can assess whether the agent is approaching it sensibly.
Adversarial testers are needed to probe how agents behave when they encounter unexpected situations, ambiguous instructions, or inputs designed to cause them to take inappropriate actions.
Why agent evaluation is a growing area of work
AI agents are being deployed into consequential professional contexts faster than robust evaluation frameworks exist for them. The gap between deployment and reliable performance is what agent evaluation work is closing.
Several factors are driving the growth of this category:
Agents operating in professional contexts, legal, financial, clinical, technical, need ongoing evaluation as they encounter new task types and failure modes in real deployments.
Regulatory interest in AI agent behaviour is increasing. Agents that take consequential actions in regulated industries are subject to scrutiny that requires documented evaluation of their behaviour.
The complexity of agent behaviour means automated testing catches fewer failure modes than it does for standard language models. Human evaluation remains more necessary for agents than it is for simpler AI applications.
The connection to standard AI evaluation
Most people who do agent evaluation work come from backgrounds in standard AI evaluation. The skills are related: careful assessment of AI behaviour, clear documentation of failures, domain expertise applied to specific task contexts.
What agent evaluation adds is the multi-step reasoning component: the ability to follow a sequence of actions and assess the quality of the overall approach, not just individual outputs. This is a more demanding skill but it builds directly on the analytical foundation that standard AI evaluation develops.
For people doing AI evaluation work now, agent evaluation is the natural next category as the market evolves. The shift from evaluating individual responses to evaluating sequences of actions is the direction the AI training market is moving.
Frequently asked questions
Do I need to understand how AI agents work technically to evaluate them? You need to understand what agents can and cannot do reliably, what their common failure modes are, and what appropriate behaviour looks like in the context you are evaluating. You do not need to understand the underlying architecture. The relevant expertise is domain knowledge combined with the ability to follow and assess multi-step task sequences.
Are agents harder to evaluate than standard language models? Yes, in general. Multi-step evaluation is more demanding than single-response evaluation. The variety of failure modes is larger. The stakes of errors are higher because agent actions can be irreversible. These factors combine to make agent evaluation a more skilled and typically better-compensated category of work than general AI evaluation.
Is agent evaluation work currently available on major platforms? It is an emerging category rather than a mature one. Some platforms have dedicated agent evaluation tracks. Others are incorporating agent evaluation into existing specialist evaluation programmes. The availability of this work will grow significantly over the next two years as agent deployment accelerates.
Summary
AI agents are systems that pursue goals across multiple steps and take actions in the world, not just produce text outputs. Training them well requires human evaluators who can assess multi-step action sequences, evaluate tool use in domain-specific contexts, and identify the diverse failure modes that agent behaviour exhibits.
The expertise requirements are higher than for standard AI evaluation and the work is growing fast as agent deployment into professional contexts accelerates. For people building AI evaluation careers now, agent evaluation is the natural direction as the market develops.