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AI Agents

Capstone: A Production-Ready Research Agent

Combine retrieval, state, approvals, evaluation, and observability into an agent that can be reviewed before release.

Advanced90 minBy ToolDix Editorial

Learning objectives

  • Deliver an evidence-backed agent with bounded authority
  • Prepare a test and operational readiness packet
  • Decide whether the system is ready for a limited release

ToolDix original visual

AI Agents practice loop
1

Frame

Name the outcome and constraints.

2

Build

Try one bounded workflow.

3

Review

Keep evidence, revise, and share.

Deliverable

Build a research assistant for an approved document set. It accepts a question, retrieves evidence, drafts a cited answer, asks for approval before any export, and stores a redacted trace. It never writes to an external system by default.

Readiness checklist

Include an architecture diagram, tool contracts, authorization matrix, data-retention table, evaluation dataset, trace example, incident response path, and rollback switch. Release only to a small internal group first.

Acceptance tests

The agent must answer supported questions with citations, abstain when evidence is missing, respect document permissions, refuse injected instructions, stop after its tool budget, and preserve a useful trace for each result.

Practice:

Run the complete acceptance set twice: once with valid evidence and once with a deliberately injected document. Compare the traces, document every failed control, and define the smallest change that closes each gap.

Reflection

Write what a human reviewer remains responsible for. If that cannot be stated clearly, the agent has too much autonomy or too little operational design.

Sources and license context

These references informed the lesson. ToolDix adds its own explanation, workflow, and practice rather than reproducing source material.

Take it further

Use a primary source to deepen this lesson.

Each recommendation is a direct link to the publisher or author. The study prompt is ToolDix editorial guidance, not copied course content.

LLM Course by Hugging Face

Hands-on lab

LLM Course

Complete the pipeline exercise, then write down what information disappears when text becomes tokens.

Open original source
Attention Is All You Need by arXiv

Classic reading

Attention Is All You Need

Read the abstract and architecture figure first; annotate what information flows between tokens.

Open original source
AI Agents Course by Hugging Face

Course

AI Agents Course

Before using a framework, write down one tool contract and the exact state an agent is allowed to change.

Open original source