A5Advanced 60 minutes reading and codeLocked

Tool Use and Function Calling

Toolformer (Schick et al., 2023) started self-supervised tool annotation. Berkeley Function Calling Leaderboard V4 (Patil et al., 2025) sets the 2026 bar: 40% agentic, 30% multi-turn, 10% live, 10% non-live, 10% hallucination. Single-turn is solved. Memory, dynamic decision-making, and long-horizon tool chains are not.

In this lesson

  1. 01Learning Objectives
  2. 02The Problem
  3. 03The Concept
  4. 04Build it
  5. 05Use it
  6. 06Exercises
  7. 07Key Terms
  8. 08Further Reading

About 1200 words of reading, with code, 7 quiz questions and the tutor.

The full lesson opens with an access code

You can see what this lesson covers and the milestone it sets. The full text, code, quizzes, the tutor and the AI reviews come with the K850 practice, along with weekly build sessions and code review.

Codes come with the K850 practice. Book a place

Book the K850 practiceRead an open lesson

Milestone forAn observable back-office agent

Tool errors the agent can recover from

Make every tool return structured errors (not found, ambiguous, rate limited, invalid input) and teach the loop to retry, ask or escalate for each. Test with a statement full of bad data.

Done when

  • Structured error types from every tool: not found, ambiguous, rate limited, invalid
  • A recovery rule per error type: retry, ask or escalate
  • A messy 50-line statement processed with no crash and every outcome recorded

Stretch: Add backoff with jitter and show it under a simulated rate limit

Reviews open with an access code.

Interview practice

Questions on this topic that AI engineering interviews ask, with the companies reported to ask them. Answer the way you would out loud; Jev scores it and DeepSeek tells you what to add.

Scoring opens with an access code. You can still read the questions and prepare.

A4The Agent Loop: Observe, Think, ActNext · A6Agent Memory — Virtual Context and Memory Paging

Adapted from open-source work: AI Engineering from Scratch by Rohit Ghumare (MIT, lesson text, code and quizzes); Awesome LLM Apps by Shubham Saboo (Apache-2.0, starter code); AI Engineering Interview Questions by Outcome School (Apache-2.0, interview questions). Capstones, milestones, data packs and Zambian context by Zambrite.

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