Why “It Works on My Prompt” Is Not Enough
AI agents are no longer just chatbots.
They:
- Call tools
- Make decisions
- Execute workflows
- Affect real systems
And yet, many teams still evaluate them like this:
“Seems to work when I try it.”
That’s not evaluation.
That’s hope.
As agents become more autonomous, evaluation becomes the hardest — and most important — part of building them.
What Makes AI Agent Evaluation Hard?
Traditional software is deterministic.
AI agents are not.
An agent:
- Interprets intent
- Chooses tools
- Handles ambiguity
- Adapts to context
Two runs with the same input can produce different outcomes — and both might look correct.
So the question changes from:
“Is the output correct?”
to:
“Did the agent behave correctly?”
What Does “Correct” Mean for an Agent?
For agents, correctness is multi-dimensional.
You’re not just evaluating answers, you’re evaluating behavior.
That includes:
- Did it choose the right tool?
- Did it follow constraints?
- Did it stop when it should?
- Did it ask for clarification?
- Did it avoid unsafe actions?
An agent that gives the right answer for the wrong reason is a future bug.
The Core Dimensions of Agent Evals
1️⃣ Task Success
The simplest signal.
- Did the agent complete the task?
- Was the final goal achieved?
This is necessary — but not sufficient.
2️⃣ Tool Usage Quality
For tool-using agents:
- Was the correct tool selected?
- Were parameters valid?
- Were unnecessary calls avoided?
Bad tool usage = fragile systems.
3️⃣ Reasoning & Decision Path
You care about:
- Order of actions
- Branching decisions
- Recovery from errors
This is where trace-based evaluation becomes critical.
4️⃣ Safety & Boundaries
Agents must respect:
- Permissions
- Data access rules
- Execution limits
A “successful” task that violates constraints is a failed eval.
5️⃣ Efficiency
More autonomy ≠ more calls.
Measure:
- Number of steps
- Token usage
- Tool invocations
- Time to completion
Smart agents are efficient agents.
Why Traditional LLM Evals Fall Short
Classic evals focus on:
- Exact match
- Semantic similarity
- BLEU / ROUGE-style scoring
Agents break these assumptions.
Two correct agents may:
- Use different tools
- Take different paths
- Produce different intermediate outputs
You need behavioral evaluation, not just output scoring.
How Modern Agent Evals Work (High Level)
A good agent eval system looks like this:
- Define scenarios (real tasks, not toy prompts)
- Run the agent end-to-end
- Capture traces (thoughts, tool calls, outputs)
- Score against multiple criteria
- Aggregate results over many runs
The key idea:
You evaluate trajectories, not just answers.
Automated vs Human Evals
Automated Evals
Good for:
- Regression testing
- Comparing versions
- Measuring efficiency
- Catching obvious failures
Examples:
- Tool-call correctness
- JSON schema validation
- Constraint checks
Human Evals
Still necessary for:
- Reasonableness
- UX quality
- Trustworthiness
- Ambiguous decision-making
The goal isn’t to remove humans — it’s to use them where they matter most.
The Role of LLM-as-a-Judge
Using one model to evaluate another is controversial — but powerful.
When done right:
- Judges evaluate reasoning quality
- Check policy adherence
- Score explanations
When done wrong:
- Bias compounds
- Mistakes reinforce themselves
LLM judges should be:
- Calibrated
- Audited
- Paired with hard constraints
Evals Are a Product Feature, Not a Research Task
This is the mindset shift.
Evals are not:
- A one-time benchmark
- A research-only activity
- Something you “add later”
They are:
- Part of your CI
- Part of your release process
- Part of your safety story
If you can’t measure agent behavior, you don’t control it.
Practical Advice for Builders
If you’re building AI agents today:
- Start with real user tasks
- Log everything (tools, steps, failures)
- Define “bad behavior” explicitly
- Track trends, not single runs
- Treat eval failures like prod bugs
Agents don’t fail loudly — they fail quietly.
Final Thought
AI agents are moving from:
“Helpful assistants”
to:
“Autonomous system components”
That transition demands rigor.
Good evals don’t slow you down.
They let you move fast without breaking reality.
If prompts are the interface,
evals are the control system.
And without control, autonomy becomes chaos.
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