Domain: AI Systems Architecture & Software Engineering (Specializing in Large Language Model Orchestration).
Persona: Senior Principal AI Systems Architect.
Vocabulary/Tone: Technical, architectural, objective, and performance-oriented.
2. Summarize (Strict Objectivity)
Abstract:
This technical synthesis outlines 20 foundational agentic design patterns designed to transition LLM implementations from basic prompting to sophisticated, autonomous systems. The patterns encompass structural workflows (Chaining, Routing, Parallelization), iterative quality controls (Reflection, Evaluation), and operational management (Resource Optimization, Memory, Exception Handling). By modularizing complex tasks into specialized sub-agents and employing rigorous feedback loops—including Human-in-the-Loop (HITL) and inter-agent communication protocols—these architectures aim to mitigate hallucinations, reduce latency, and manage computational costs in production-grade environments.
Architectural Summary of Agentic Design Patterns:
0:53 Prompt Chaining: Decomposes monolithic tasks into a linear sequence of subtasks. Each node validates the output of the preceding step before execution continues, creating an "assembly line" effect that localizes failures.
5:42 Routing: Employs an intent-analysis agent to direct user requests to specialized domain agents (e.g., technical support vs. sales). It utilizes confidence scores to trigger clarification loops when intent is ambiguous.
9:26 Parallelization: Increases throughput by splitting large inputs into independent segments processed simultaneously by multiple worker agents. Outputs are subsequently normalized and merged into a unified final response.
13:12 Reflection: Implements a generator-critic loop where an initial output is reviewed against specific quality rubrics. The system iterates through revisions until the content satisfies predefined success criteria.
15:51 Tool Use: Enables agents to interact with external environments (APIs, databases, web search). The pattern includes discovery, permission verification, and output normalization for LLM consumption.
18:23 Planning: Converts high-level goals into a dependency graph of milestones. The agent monitors environmental constraints (budget, deadlines) and adapts the execution path as internal or external variables shift.
20:51 Multi-agent Collaboration: Utilizes an orchestrator to manage specialized roles (e.g., sound vs. lighting) sharing a common script or state. This requires a robust coordination protocol to manage acceptance testing across agent boundaries.
23:44 Memory Management: Categorizes data into short-term (context window), episodic (specific events), and long-term (permanent knowledge). Strategies include context compression and vector indexing for relevant retrieval.
26:40 Learning & Adaptation: Establishes a feedback loop where user ratings and automated evaluations are used to update system prompts, few-shot examples, or model policies to improve future performance.
29:11 Goal Setting & Monitoring: Defines measurable (SMART) objectives and tracks KPIs throughout execution. The system triggers re-calculation or escalation if the agent drifts from the intended target.
31:30 Exception Handling & Recovery: Standardizes error classification (temporary vs. permanent). It employs strategies like exponential backoff for API timeouts and "Plan B" fallbacks for critical service failures.
34:11 Human-in-the-Loop (HITL): Introduces manual intervention points for high-stakes decisions or low-confidence edge cases. The UI presents full context to the human reviewer for approval, editing, or override.
36:03 Knowledge Retrieval (RAG): Enhances groundedness by parsing and embedding external documents into vector databases. The pattern involves query rewriting, top-K retrieval, and reranking to optimize context relevance.
38:14 Inter-agent Communication: Standardizes how agents exchange data using structured messaging (IDs, expiration times). Architectures vary from hierarchical (one boss) to democratic (peer-to-peer) or blackboard-based (shared thread).
43:04 Resource-aware Optimization: Analyzes task complexity to route requests to the most cost-effective model. Simple tasks utilize small/fast models, while reasoning-heavy tasks utilize high-parameter, expensive models.
46:36 Reasoning Techniques: Matches problem types to specific logic patterns, such as Chain-of-Thought (sequential), Tree-of-Thought (branching/pruning), or Adversarial Debate (proponent vs. opponent).
49:54 Evaluation & Monitoring: Establishes quality gates and "golden test" sets. It monitors production performance for "drift"—where model responses degrade or deviate over time.
52:46 Guardrails & Safety: Screens inputs and outputs for PII (Personal Identifiable Information), injection attacks, and malicious content. High-risk inputs are sanitized, redacted, or blocked before reaching the core logic.
56:01 Prioritization: Scores tasks based on a formula involving business value, urgency, effort, and risk. The queue is dynamic, meaning priorities are re-evaluated after each task completion.
59:28 Exploration & Discovery: A research-centric pattern that broadly scans data sources to identify clusters and themes. It identifies novelty and knowledge gaps before targetting deep investigations and hypothesis generation.