Your AI agent is brilliant. It can write code, analyze documents, and answer complex questions with remarkable sophistication.
It is also a goldfish. Every conversation starts from scratch. Every user is a stranger. Every context is new.
Google just released a whitepaper on context engineering that tackles this fundamental problem. The paper introduces a systematic framework for making LLM agents stateful using two core primitives: Sessions and Memory.
The framework formalizes the architectural patterns that separate toy demos from production AI systems.
The statelessness problem
LLMs are fundamentally stateless. Outside their training data, their awareness is confined to the immediate context window of a single API call.
You can craft the perfect prompt, tune every parameter, and still end up with an agent that forgets the user’s name between conversations. The model doesn’t remember. It doesn’t learn. It processes each turn in isolation.
Context Engineering is the discipline of dynamically assembling and managing all information within that context window to make agents stateful and intelligent. It is prompt engineering evolved: shifting from crafting static instructions to constructing the entire state-aware payload for every turn.
The business impact is direct. Stateless agents can’t personalize. They can’t maintain coherent multi-turn workflows. They can’t reduce repetitive questions or remember user preferences.
Context Engineering Framework (Google Whitepaper)
│
├─ Core Primitives
│ ├─ Sessions (temporary workbench)
│ └─ Memory (long-term filing cabinet)
│
├─ Key Distinctions
│ └─ Memory vs RAG
│
├─ Production Challenges
│ ├─ Latency & cost
│ ├─ Data isolation
│ └─ Memory poisoning
│
└─ Advanced Concepts
│ ├─ Memory provenance (trust layer)
│ └─ Procedural memory (workflows)Sessions: The temporary workbench
A Session is the container for a single, continuous conversation. Think of it as the workbench where the agent does its immediate work.
Every Session contains two parts. First, the chronological event log (user inputs, agent responses, tool outputs). Second, the temporary working memory or state (like items in a shopping cart or the current step in a workflow).
The core operational challenge is managing growing conversation history. Long context creates four production problems: exceeding the model’s context window limit, escalating API costs (charged per token), increasing latency, and degrading model performance (“context rot”).
This is where compaction strategies become critical. Simple approaches truncate old messages after a token limit. Sophisticated systems use recursive summarization, where an LLM periodically condenses older conversation segments into compact summaries.
Here’s the trade-off in practice. A customer support agent handling 50 turns would send thousands of tokens per request without compaction. With recursive summarization (triggered every 20 turns), the system replaces verbose dialogue with a summary: “User confirmed Order 456 had missing item, requested refund.” Context preserved, costs and latency slashed.
Memory: The long-term filing cabinet
If Sessions are the temporary desk, Memory is the meticulously organized filing cabinet. This is where the personalization value lives.
Memory captures and consolidates key information across multiple sessions. It transforms agents from chatbots that reset every conversation into assistants that remember your preferences, context, and history.
The architecture is typically a combination of vector databases (for semantic similarity and unstructured facts) and knowledge graphs (for structured relationships and reasoning). But the real sophistication is in how memories get created and maintained.
Memory generation is an LLM-driven ETL pipeline. Extract meaningful content from conversations. Transform and consolidate it by handling conflicts and duplicates. Load the refined knowledge into persistent storage.
The consolidation stage is where most systems fail. Without it, memory becomes a noisy, contradictory log. With proper consolidation, the system compares new insights against existing memories, decides whether to update, create, or delete entries, and actively prunes stale information.
The critical distinction: Memory vs RAG
Product teams often conflate Memory with Retrieval-Augmented Generation (RAG), but they serve fundamentally different roles.
RAG injects static, factual knowledge from external sources (PDFs, wikis, documentation). It makes the agent an expert on facts. The data is typically shared across all users and read-only.
Memory curates dynamic, user-specific context derived from conversation. It makes the agent an expert on the user. The data must be highly isolated per user to prevent leaks.
Think of RAG as the research librarian providing universal knowledge. Memory is the personal assistant who knows your preferences, history, and context. Both are essential, but they operate at different layers of the system.
Here’s the strategic framework at a glance:
| Component | Function | Core Value | Key Risk |
|---|---|---|---|
| Sessions | Temporary workbench for current conversation | Coherent, contextual dialogue in the present turn | Latency and cost overrun from long context |
| Memory | Long-term filing cabinet across conversations | User personalization and persistent knowledge | Memory corruption, data leaks, blocking latency |
Production challenges you can’t ignore
The whitepaper highlights three critical production risks that context engineering must address.
Latency and blocking UX. Memory generation requires expensive LLM calls and database writes. If this runs synchronously (blocking the user response), the experience becomes unacceptably slow. The solution is to handle memory operations asynchronously as background processes after responding to the user.
Data isolation and privacy. Sessions and Memory must enforce strict per-user isolation. A user cannot access another’s conversation data or memories. PII should be redacted from session data before persistence to mitigate breach risks.
Memory poisoning. Malicious users can attempt to corrupt the knowledge base by feeding false information. Safeguards like validation, sanitization, and trust scoring (memory provenance) must be employed before committing data to long-term memory.
These aren’t edge cases. They’re the difference between a demo that works in development and a system that scales in production.
Memory provenance: The trust layer
Not all memories are equally reliable. Some come from explicit user statements (“I prefer aisle seats”). Others are inferred from implicit behavior or bootstrapped from external systems like CRMs.
Memory provenance is the detailed record of a memory’s origin and history. Each memory carries metadata about its source, confidence score, and how that confidence changes over time (increasing with corroboration, decaying with age).
During consolidation, when new information conflicts with existing memories, provenance establishes a hierarchy of trust. A fact from a high-trust CRM system might override casual user dialogue. At inference time, the confidence scores are injected into the prompt, allowing the LLM to weigh evidence and make nuanced decisions.
This is the difference between a memory system that amplifies errors and one that becomes more accurate over time.
Procedural memory: Learning how, not just what
Most memory systems focus on declarative memory (facts and events). The whitepaper emphasizes procedural memory: the agent’s knowledge of skills and workflows.
Procedural memory captures the correct sequence of tool calls, the optimal strategy for recurring tasks, or the playbook for handling specific scenarios. It is extracted from successful interactions and distilled into reusable patterns.
The value is online adaptation. Instead of the slow, expensive process of fine-tuning model weights offline, procedural memory provides fast adaptation by injecting the right plan into the context via in-context learning.
For product teams, this means agents can learn and improve their workflows without requiring model retraining. That’s a significant operational advantage.
What this means in practice
For teams building stateful AI agents, the whitepaper provides a clear architectural roadmap.
Session management starts with conversation history persistence and compaction strategies. Simple token-based truncation handles basic cases. More sophisticated systems use recursive summarization to preserve context while controlling costs. Storage must be robust, retrieval fast, and per-user isolation strict.
Memory systems layer in gradually. Declarative memory (user preferences, key facts) provides the foundation. Asynchronous memory generation prevents blocking latency. Consolidation logic handles conflicts and prunes stale data.
Provenance tracking establishes trust and enables conflict resolution.
The architectural choices matter. Teams treating context engineering as foundational infrastructure get personalized, reliable agents. Those treating it as an afterthought face escalating costs, latency issues, and degraded user trust.
The shift that matters
Context Engineering represents the maturation of AI agent development. It moves the focus from crafting clever prompts to building robust systems that manage state, persist knowledge, and adapt over time.
The primitives are clear: Sessions for immediate coherence, Memory for long-term personalization. The challenges are well-defined: managing context length, ensuring data isolation, building trust through provenance.
Google’s whitepaper formalizes what production AI teams have been learning through experience. Not every use case requires stateful agents. But for applications where personalization, workflow continuity, or multi-turn context matters, this framework provides the architectural foundation.
The distinction between Sessions and Memory, the emphasis on consolidation and provenance, the recognition of procedural memory as distinct from declarative facts: these concepts clarify the design space and highlight the trade-offs that matter in production.