1.3 — Agent Patterns and Frameworks

CCDV-F Domain 1 · Task 1.3

What You Need to Know

The Five Workflow Patterns

Pattern	Mechanism
Prompt chaining	Fixed sequence; each call works on the previous output; optional programmatic gates
Routing	Classify input and send to specialised follow-up (different prompt, tool set or model per category)
Parallelization	Sectioning (independent subtasks side by side) or voting (same task several times for diverse answers)
Orchestrator-workers	Central model breaks the task down at runtime and delegates; subtasks not known in advance
Evaluator-optimizer	Generator + evaluator that scores against explicit criteria; critique loops back until pass

Telling the Patterns Apart

• Chaining vs routing: fixed sequence vs classification decision
• Sectioning vs orchestrator-workers: subtasks known in advance vs decided at runtime
• Voting vs evaluator-optimizer: independent repeated attempts aggregated by code vs feedback loop with critique
• Gate vs evaluator-optimizer: fixed pass/fail in a one-way sequence vs looped critique

Memory Is a Design Decision

An agent remembers nothing between sessions unless the design adds a mechanism. Anthropic’s memory tool (memory_20250818) gives create/read/update/delete over files in /memories. Your application executes each operation client-side. Safeguards live in your handler (reject path traversal, track file sizes, cap view returns).

Context as a Constraint

Context rot: recall degrades as token counts grow. Documented moves: compaction, structured note-taking, sub-agent architectures. Compaction summarises and starts a new window with the summary (Agent SDK does this automatically).

Framework Identities

• LangGraph — low-level orchestration; model control flow as a graph; durable executions; human-in-the-loop
• Pydantic AI — typed Python agent SDK; validated output types
• Strands Agents — model-driven loop; minimal orchestration code; from Amazon production systems

Key Concept: The requirement profile justifies a pattern, never sophistication. Plain code beats a framework for a fixed pipeline.

Exam Traps

• Choosing sectioning when the model must discover subtasks at runtime (or vice versa)
• Assuming an agent keeps yesterday’s decisions between sessions by default
• Recommending LangGraph for typed output validation, or Pydantic AI for graph-shaped control flow
• Choosing voting when the requirement is a critique fed back into the next draft