Debate & Critic Agents
Purpose
[stub: debate-and-critic-agents]
Metadata
| Author | Amit Singh |
| Scope | agentic-ai-engineering |
Local graph
Linked from 4 notes
5. Agent Negotiation
Covers how agents with different objectives or partial information reach agreement on a shared action, including bidding and argumentation-based negotiation protocols.
2. Decision Making
Covers the decision-making layer that selects the next action from the perceived state — utility scoring, rule-based gating, and LLM-driven choice — and how confidence and risk thresholds shape when an agent should act versus escalate.
Agentic AI Engineering
A book-shaped table of contents for Agentic AI Engineering: where 'LLM application' becomes 'agent' — introduction to agentic AI, agent cognition, memory systems, planning & reasoning algorithms, tools & environment interaction, retrieval & knowledge systems, and context engineering. Book 2 of the AI Systems Engineering series.
2. Prompt Design Patterns
Catalogs reusable prompt patterns — chain-of-thought, ReAct, self-consistency, and role/persona framing — with guidance on when each pattern earns its added token cost over a plain instruction.
Related notes
1. Chain of Thought
Covers Chain-of-Thought prompting -- eliciting intermediate reasoning steps before a final answer -- why it improves multi-step task performance, and its limits on tasks requiring backtracking or exploration.
5. Graph of Thoughts
Covers Graph-of-Thoughts reasoning, where intermediate thoughts can merge, refine, and feed back into each other as a DAG rather than a tree, and the problem classes where this generalization pays off over Tree-of-Thoughts.
11. Hierarchical Planning
Covers hierarchical planning -- decomposing a goal into subgoals handled by higher- and lower-level planners at different abstraction levels -- and how this scales agent reasoning to long-horizon tasks that flat planning approaches struggle with.
9. LLM Compiler
Covers the LLM Compiler pattern -- planning a DAG of tool calls upfront and executing independent branches in parallel -- and the latency and cost wins over sequential ReAct-style execution for tasks with parallelizable sub-steps.