information flow architecture

MCP is fundamentally about designing how information flows between AI systems and external services. Standardization ensures clarity in this flow.

Sequential pattern is a specific information flow topology; clarity about flow structure enables debugging when context degrades across agents

Benchmarks two distinct information flow topologies (parallel consolidation vs sequential handoff), revealing that architecture choice measurably impacts task performance.

Each orchestration pattern creates a different information flow topology, which is central to context engineering

MCP servers define how information flows between Claude and external systems, a critical context architecture decision

Shows intentional asymmetry in information flow: generator doesn't see review feedback (naturally), but reviewer also intentionally doesn't see generation decisions

Centralized vs decentralized control structures directly determine how information (context) flows between agents and to/from the orchestrator

Describes how data moves through perception → reasoning → execution pipeline, with quality degradation risks

Discussion of 'data flow and coordination between services' is essentially information architecture design, a core context engineering concern.