As AI agents advance from simple chatbots to autonomous systems handling complex, long-running tasks, organizations encounter a major hurdle: managing context efficiently. Relying on ever-larger context windows in language models quickly becomes unsustainable, leading to high costs, increased latency, and overwhelming information.
To overcome these challenges, industry leaders like Google are embracing context engineering, a discipline that treats context as a system with its own architecture, lifecycle, and optimization strategies.
The Compiler Approach to Context
Instead of viewing context as a flat, ever-growing text block, modern frameworks such as the Google Agent Development Kit (ADK) approach context as a compiled view over a structured, stateful system. This paradigm introduces:
- Sessions, memory, and artifacts as reliable, durable sources of truth.
- Flows and processors that serve as a compilation pipeline, transforming stored state into actionable context for each agent invocation.
- A working context, a targeted, ephemeral snapshot created specifically for each model call.
This structured model enables agents to access relevant information with precision, turning context management into a systems engineering problem rather than just prompt design.
Architectural Pillars of Modern Context Management
To support the shift from simple chatbots to scalable, autonomous agents, developers must treat context as a structured engineering problem rather than a mere text buffer. This modern approach is built upon distinct architectural pillars that organize information into a reliable, efficient system.
By decomposing context into specialized layers and pipelines, these pillars enable agents to maintain state, recall long-term history, and optimize inference costs without overwhelming the model
Tiered Context Model
ADK organizes context into layered components:
- Working context: Immediate, per-invocation details assembled from session logs, tool outputs, and selected memory.
- Session: A persistent, structured log that records every interaction, tool use, and system action.
- Memory: Searchable, long-term knowledge that spans across sessions, such as user preferences or past conversations.
- Artifacts: Large files referenced by handle, included in the prompt only when needed.
This separation allows teams to evolve storage and prompt formats independently, supporting efficient, model-agnostic context assembly.
Context Pipelines: Flows and Processors
Context in ADK is constructed by sequential processors, modular units that filter, transform, and inject relevant data. This pipeline approach:
- Enables custom strategies for compaction, filtering, and caching.
- Improves visibility and testability of context changes.
- Eliminates rigid, monolithic prompt templates.
Efficiency: Compaction, Filtering, and Caching
To keep context windows manageable, ADK leverages LLM-powered context compaction and rule-based filtering. Older events are summarized at the session layer, ensuring that context remains concise and inference stays fast. Caching further boosts efficiency by separating stable instructions from dynamic inputs and using model-level prefix caching.
Ensuring Relevance in Context
For effective agent performance, relevance is essential, agents should only access information pertinent to their current step. ADK blends human-designed rules with agent-driven retrieval by:
- Referencing artifacts by handle, loading them on-demand to keep context slim.
- Retrieving memory semantically, either in response to identified gaps or proactively by the system.
This method moves away from “context stuffing,” enabling precise and efficient data recall.
Multi-Agent Coordination: Scoped Context and Hand-Offs
In multi-agent environments, simply passing full histories leads to context explosion. ADK addresses this with explicit scoping, sub-agents receive only the context they need, preventing confusion and inefficiency. Flexible handoff rules support both simple tool-like calls and more complex agent transfers, with ADK actively translating roles and attributing actions to maintain clarity and prevent misattribution.
Takeaway: Context as a Core Engineering Discipline
Robust, efficient context management is now a vital requirement for production AI agents. By adopting a tiered, pipeline-driven, and scoped architecture, demonstrated by Google ADK, organizations can move beyond fragile prototypes to scalable, maintainable, and cost-effective agent systems. Context engineering is now as integral as storage or compute in the AI development stack.
Source: Google Developers Blog
Why Context Engineering Is Key for Scalable Multi-Agent AI Systems