agent architecture patterns

We design intelligent systems where specialized AI agents collaborate in parallel — not single-model solutions, but orchestrated workflows governed by explicit roles, validation gates, and human accou

The four strategies (write, select, compress, isolate) are architectural patterns for building production-grade agents that maintain coherence across long tasks

Most agents underperform because they lack context. This prompt forces you to see the gaps and fix them." — Provides evidence that systematic context gap identification is a key architectural pattern

The thin harness/thin skills/skilled operator model is a specific architectural pattern for distributing intelligence across agent components.

Both Manus (four rebuilds of agent framework) and Anthropic's guide focus on context engineering for agents specifically. Agent architecture and context engineering are tightly coupled.

The markdown files (skills, memory, tool instructions) are architectural components of agent systems; their ordering is an architectural decision

Highlights that agent architecture choices (SDK-based vs model-agnostic) have hidden costs that aren't obvious at development time.

Post identifies two divergent agent architecture patterns (embedded vs pluggable) that will have different context management requirements

Understanding memory types is foundational to choosing agent architecture—whether to implement retrieval augmentation, episodic memory, semantic memory, or explicit knowledge bases.

Context engineering for agents differs from single-turn context; agents require compounding knowledge across tool calls and steps

[INFERRED] "Learn to design and build AI agents today" — Course focuses on teaching systematic design and architecture patterns for production-ready AI agents

Current agent frameworks operate in pull mode. Moving to continuous context reactivity would require new agent orchestration patterns—how agents maintain and update context over time.

Survey of context patterns for agents contributes to broader agent architecture design space

Describes a specific architectural pattern for tool registration in agent systems

The concrete examples (coding agents over mono repos, fuzzy product search) show how retrieval design is a foundational agent architecture decision

pricing, data sources, benchmarks" — Systematic evaluation of search API alternatives (pricing tiers, data coverage, performance) supports architectural decision-making for agent design

Article implies agentic architecture is inherently superior without addressing the context management complexity that makes agents fragile. Real agents fail due to context degradation, not because the

The combination of MCP + codemode + elicitations suggests a deliberate multi-component architecture for autonomous agents

Attempts to classify agent design approaches but at conceptual rather than implementation level

Title references agent architecture and mental models, but incomplete content prevents confirming what specific patterns are discussed

Author references agents but without describing architectural decisions, context management approaches, or how building vs using agents relates to context preservation