Implements a TOOL_REGISTRY dispatcher with canonicalize_args, missing_required detection, and resolve_args resolution for LLM-invoked tools. This is a genuinely well-structured tool-dispatch layer: an agent can enumerate available tools, the registry validates and canonicalizes arguments before invocation, and missing-required checks provide clean error feedback loops for the LLM. The pattern is framework-agnostic and could be lifted into any agent project needing deterministic tool routing with argument normalization.

Contains execute_plan_with_reflection, an orchestration loop where an LLM generates a multi-step Python plan, the plan is executed against a TinyDB/DuckDB inventory, execution results and errors are fed back to the LLM, and the plan is revised until success. This is a clean, self-contained implementation of the 'code as plan' autonomous-agent pattern — the reflection loop (execute → observe → revise) is reusable across many domains and demonstrates non-obvious agent engineering.

A compact set of email tools (send, search, filter by sender, delete) wrapped around a simulated REST email API. Each function has a rich, LLM-oriented docstring describing inputs, outputs, and failure semantics — exactly the contract LLM tool-calling needs. The module demonstrates the standard 'define function with docstring → expose to LLM' pattern that underlies all major agent frameworks, and could be extracted as a reference implementation for wrapping any external API as agent tools.

Provides create_transactions_db, get_schema, and execute_sql helpers that build a synthetic but realistic event-sourced transactions database (insert, restock, sale, price_update events) and expose its schema for natural-language-to-SQL generation. Combined with the lab's reflection workflow in the notebook (generate_sql → execute → feed results/errors back to LLM → refine_sql_external_feedback), this is a compact, reusable implementation of the external-feedback reflection pattern — teaching how actual query execution output can correct LLM semantic errors. The schema-introspection approach is directly portable to any NL-to-SQL agent.