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References: Designing AI Agents and Expert Roles

  1. Intelligent agent - Wikipedia - Offers an accessible overview of Intelligent agent, including definitions, methods, examples, limitations, and related concepts. This foundation helps students reason carefully about defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  2. Multi-agent system - Wikipedia - Offers an accessible overview of Multi-agent system, including definitions, methods, examples, limitations, and related concepts. Its examples help students evaluate evidence for defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  3. Role-based access control - Wikipedia - Offers an accessible overview of Role-based access control, including definitions, methods, examples, limitations, and related concepts. It supplies useful context for decisions about defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  4. An Introduction to MultiAgent Systems (2nd ed.) - Michael Wooldridge - Wiley - Introduces autonomous agents, environments, interaction, cooperation, communication, and organizational structures with formal and practical examples. Its sustained treatment supports work on defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  5. Human Compatible - Stuart Russell - Viking - Examines machine objectives, uncertainty, control, human preferences, and the governance problems created by increasingly capable systems. Its cases illuminate tradeoffs involved in defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  6. Agents - OpenAI - Explains how models, tools, knowledge, instructions, guardrails, and orchestration combine into bounded agent workflows that can be tested and monitored. Its methods give teams a starting point for defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  7. Building Effective Agents - Anthropic - Distinguishes workflows from agents and illustrates routing, parallelization, orchestration, evaluation, tool design, and appropriate control of system complexity. Its comparisons clarify choices involved in defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  8. AI Agent Orchestration Patterns - Microsoft Learn - Compares sequential, concurrent, group-chat, handoff, and magentic orchestration patterns, including their coordination costs, failure modes, and suitable use cases. Its framework strengthens responsible work on defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  9. AI Risk Management Framework - NIST - Provides the Govern, Map, Measure, and Manage functions for addressing validity, reliability, transparency, privacy, fairness, accountability, and other AI risks. Its examples show how evidence informs defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.

  10. Demystifying Evals for AI Agents - Anthropic - Explains agent evaluation design, realistic tasks, outcome and process graders, repeated trials, transcript review, and analysis of variable behavior. Its implementation advice helps teams practice defining bounded agents with explicit roles, goals, tools, autonomy, memory, and outputs.