#architecture

5 resources and 3 guides tagged #architecture on ChangeGamer.

Agent Memory and Context Management · Guide
Architecture reference for agent memory: types (working, long-term, episodic, semantic, procedural), context-management techniques (summarization, RAG, sliding windows, prompt caching), storage substrates, and memory frameworks — with security notes and cross-links to related guides.
Multi-Agent Orchestration Patterns · Guide
Vendor-neutral reference covering when multi-agent systems pay off and nine named patterns — from single-agent baseline through hierarchical and blackboard architectures — with tradeoffs, cross-cutting concerns, and a decision guide.
Agent Cost and Latency Optimization · Guide
Practitioner reference for reducing the cost and latency of production AI agents: the compounding model, token-level levers (caching, pruning), request-level levers (Batch API, parallelism), model-level levers (routing, reasoning-effort controls), and architecture-level levers (step reduction, semantic caching, code offloading).
MCP vs Function Calling: When to Use Which · Reference
Direct comparison of provider-native function/tool calling and the Model Context Protocol — architecture, decision criteria, and how they compose.
Prompt Injection Design Patterns: Architectural Defenses for Agents · Guide
Six named architectural patterns — Action-Selector, Plan-Then-Execute, LLM Map-Reduce, Dual LLM, Code-Then-Execute, Context-Minimization — plus Google DeepMind's CaMeL, that structurally constrain what an agent can do with untrusted data instead of just filtering it.

Guides

Defending MCP Clients Against Tool Description and Output Injection · guide
Two distinct MCP injection surfaces — a tool description at connect-time and a tool's return value at call-time — and the client-side architectural patterns (Dual LLM, Action-Selector, Context-Minimization) that contain each one.
MCP Tools vs Resources vs Prompts: How to Choose the Right Primitive · guide
A decision procedure for MCP's three server-side primitives — who controls each one, a worked example of what it costs to expose a Resource as a Tool by mistake, and how Sampling and Elicitation fit as the client-side counterparts.
How to Defend an AI Agent Against Prompt Injection · guide
A decision framework for matching Action-Selector, Plan-Then-Execute, Dual LLM, and the other named architectural patterns to a task's actual blast radius, including when to compose two patterns together and when none of them is worth the overhead.