Architecting efficient context-aware multi-agent framework for production

Abstraction: Context engineering architecture in Google ADK agents

Key points:

• Google's Agent Development Kit (ADK) treats "context engineering" as systems engineering, not prompt gymnastics; core thesis: context is a compiled view over a richer stateful system, not a mutable string buffer.
• Tiered storage separates Session (ground-truth, strongly-typed Event records) from the ephemeral Working Context (a computed projection rebuilt each invocation via ordered request/response processors in an LLM Flow).
• Context Compaction: at a configurable threshold, an async LLM summarizes older events over a sliding window and writes a "compaction" event, letting raw events be pruned. Filtering offers deterministic rule-based reduction.
• Optimizations: prefix/context caching via static instruction immutability keeps cache prefix valid; Artifacts (handle pattern via ArtifactService + LoadArtifactsTool) keep large data (5MB CSVs, PDFs) out of the prompt with ephemeral expansion; Memory (MemoryService) provides searchable, agent-directed long-term knowledge over a vector/keyword corpus.
• Multi-agent scoping prevents context explosion: two patterns — Agents-as-Tools (callee sees only focused prompt) and Agent Transfer/Hierarchy (control handed off). include_contents knob controls how much history flows; ADK reframes prior agents' messages as [For context]: Agent B said... so a sub-agent doesn't misattribute actions to itself.

Connections: Google ADK · Context Engineering · Multi Agent Systems · Prompt Caching · AI Agents

Source: https://developers.googleblog.com/en/architecting-efficient-context-aware-multi-agent-framework-for-production/