Lesson 1595 of 2116
Tool-Use Evaluation: Building Reliable Agent Benchmarks
Tool-use evals must capture argument correctness, sequencing, and recovery from tool errors — not just whether the model called the tool at all.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Tool Use and Function Calling Internals: How AI Models Decide to Call Code
- 3The premise
- 4AI and Tool Use Schema Design: Function Definitions That Work
Concept cluster
Terms to connect while reading
Section 1
The premise
AI can design tool-use eval suites that score argument correctness and recovery, but engineering must integrate them into CI.
What AI does well here
- Generate tool-use eval scenarios across success, partial-success, and failure paths.
- Draft argument-correctness scoring rubrics.
What AI cannot do
- Decide what error-recovery quality is acceptable.
- Replace human review of edge-case behaviors.
Key terms in this lesson
Section 2
Tool Use and Function Calling Internals: How AI Models Decide to Call Code
Section 3
The premise
Function-calling models are trained to route between generating text and invoking a tool by emitting structured tool-call tokens.
What AI does well here
- Choose between candidate tools when the schema is well-specified
- Generate well-formed argument JSON for known tools
- Compose multi-step tool calls when the task structure is in-distribution
What AI cannot do
- Recover gracefully when no available tool fits the user's request
- Calibrate tool-call confidence reliably across novel domains
- Replace deterministic routers when correctness requirements are absolute
Section 4
AI and Tool Use Schema Design: Function Definitions That Work
Section 5
The premise
Tool schemas fail at the description field; AI rewrites them to maximize correct invocation.
What AI does well here
- Draft tool descriptions optimized for clarity
- Suggest parameter names that reduce ambiguity
- Format error-return shapes the model can recover from
What AI cannot do
- Guarantee the model never hallucinates an argument
- Test all real-world tool combinations
Section 6
Tool Use and Function Calling: How AI Reaches Outside Itself
Section 7
The premise
Tool use lets a model emit a structured request to call a function — search the web, query a database, send an email — that your code then executes. This is the foundation of every modern AI agent.
What AI does well here
- Letting the model decide when to call a calculator vs answer from memory
- Connecting the model to live data via search or database tools
- Producing well-formed JSON arguments matching a tool schema
- Chaining multiple tool calls within a single response
What AI cannot do
- Guarantee the model picks the right tool — it can hallucinate parameters
- Replace good schema design — vague schemas produce vague calls
- Eliminate the need for input validation on every tool call
End-of-lesson quiz
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