Context Engineering for AI

8/6/2026

This post introduces Redis Agent Memory, a new capability that provides persistent memory for Snowflake Cortex AI agents. It details the four types of memory (episodic, semantic, user preference, procedural) and how they are utilized to enhance agent context. The post highlights the integration with Snowflake Marketplace and Snowpark Container Services, exposing SQL functions for memory management and prompt hydration, and emphasizes the benefits of keeping data within the Snowflake account.

8/5/2026

This post expands on the 'Context Engineering for AI' feature thread by providing a comparative analysis of Redis as a vector search solution against dedicated vector databases and PostgreSQL with pgvector. It details the architectural benefits of a unified platform like Redis for RAG pipelines, focusing on reduced infrastructure overhead, operational simplicity, and cost savings through semantic caching. It also introduces and briefly describes several other vector database alternatives, positioning Redis within the broader landscape of AI data infrastructure.

8/4/2026

This post elaborates on the 'Context Engineering for AI' thread by focusing specifically on semantic memory search as a mechanism for AI agents to achieve durable recall. It details the architecture of semantic memory search (storage of facts as vector embeddings and retrieval via similarity search), explains why AI agents forget without an external memory layer (context window limits, cost, accuracy degradation), and contrasts semantic search with keyword search. It also emphasizes the critical need for fast and fresh memory retrieval, discussing latency considerations for interactive interfaces and voice agents, and the problem of data staleness in memory. The post highlights Redis Iris and Redis Agent Memory as solutions for providing low-latency, real-time context and memory for AI agents, including support for hybrid search and a two-tier design for session memory.

8/3/2026

This post elaborates on the challenges of connecting AI agents to data sources, focusing on integration sprawl, stale data, and permissions/data governance. It emphasizes the need for a context engine to manage data freshness and speed on the agent's hot path, introducing Redis Iris as a solution for providing real-time context. The post also discusses various patterns for connecting agents to data, including RAG, tool/function calling, MCP, and custom API connectors, and highlights the importance of data quality and retrieval strategies for agent output quality.

7/29/2026

Introduces and defines 'context engineering' for AI systems, differentiating it from prompt engineering. It details the challenges in AI agent reliability stemming from context management, categorizing failures into fragmentation, opacity, speed degradation, and non-accumulation. The post proposes four core operations for context engineering: write, select, compress, and isolate. It also outlines infrastructure needs, including support for multiple query modalities and real-time data freshness, and presents a five-stage maturity model for evaluating context layers. Finally, it explains how Redis, particularly through Redis Iris, can serve as a real-time context engine by integrating storage, retrieval, and messaging primitives.

7/28/2026

This post details four failure modes in AI agent context layers: fragmentation (data scattered across systems), opacity (agents can't access or interpret data), speed degradation (latency compounding across retrieval loops), and non-accumulation (context not building over time). It proposes solutions including real-time data synchronization via change data capture, navigable context layers with defined data models and access controls (e.g., Redis Iris and Context Retriever), and high-performance data stores (e.g., Redis) to mitigate these issues. The post emphasizes the importance of a production-ready context engine for reliable AI agents.

7/28/2026

Introduces token-budget-aware LLM reasoning (TALE) as a cost-saving mechanism. Details prompt-level techniques like Chain-of-Thought (CoT), Chain of Draft, and TALE Estimation + Prompting, including their effectiveness and limitations. Discusses API-level budget controls in Gemini and Claude. Proposes architectural solutions: semantic caching (with Redis Iris/LangCache) for repeated queries and complexity-based routing for model selection. Highlights Redis Iris/LangCache for semantic caching and RedisVL SemanticRouter for routing.

7/26/2026

This post elaborates on the 'reranking' aspect of context engineering for AI. It provides a technical breakdown of reranking models, including their architectures (cross-encoders, LLM-based, late-interaction), deployment considerations (open-weight vs. API-hosted), and practical advice on selection criteria (accuracy vs. latency, context length, multilingual needs, score calibration). It also highlights the interplay between retrieval and reranking, and mentions Redis Iris and Redis Search's FT.HYBRID command as relevant infrastructure components.

7/22/2026

This post analyzes five agent architecture scenarios to determine the applicability of MCP and A2A protocols. It details how MCP is used for agent-to-tool integration and A2A for agent-to-agent collaboration, providing specific examples for each scenario. The post also delves into the security implications of each protocol, noting MCP's current limitations in authorization and A2A's stronger focus on identity and transport-layer security. It concludes that protocols should be adopted based on the specific boundaries being crossed rather than trending popularity.

7/22/2026

This post elaborates on the concept of context assembly in LLM applications, detailing the components that form a prompt (system instructions, retrieved knowledge, conversation history, long-term memory, tool definitions) and the challenges associated with managing them within token limits. It introduces Redis Iris as a solution for low-latency context assembly, highlighting its capabilities in providing real-time context through Redis Agent Memory and semantic caching with Redis LangCache. The post also discusses the impact of input ordering on model performance and cost, and the failure modes associated with context management.

7/22/2026

Introduces and contrasts Model Context Protocol (MCP) and Agent2Agent (A2A) protocols for agent communication. MCP is described as an open standard for connecting LLM apps to external tools and data, using a hierarchical client-server model with roles for hosts, clients, and servers, and primitives like tools, resources, and prompts. A2A is presented as a protocol for communication between independent agents, featuring Agent Cards for discovery, stateful Tasks, and Artifacts, supporting peer-to-peer interaction and longer-running tasks. The post clarifies that MCP is for agent-to-tool integration and A2A is for agent-to-agent collaboration, with the decision hinging on agent ownership and trust boundaries. It also highlights that neither protocol fully addresses state correlation across workflows or end-to-end observability.

7/21/2026

This post introduces a debugging guide for RAG systems, focusing on five common failure categories: retrieval, ranking, grounding, freshness, and latency. It details specific causes within each category, such as chunking strategy impact on retrieval, vocabulary mismatch, the role of reranking and hybrid search, LLM grounding issues like context poisoning and distraction, freshness problems due to stale indexes and duplicates, and latency disguised as accuracy issues. It also highlights Redis's capabilities in real-time data integration and incremental vector index updates for addressing freshness and latency.

7/15/2026

This post details the problem of stale context in multi-step AI agents, explaining how outdated information can lead to incorrect actions and policy violations. It identifies several failure modes, including cached answers, prompt caching, early errors becoming ground truth, and silent truncation. The post frames freshness as a context engineering problem, not just a RAG problem, and outlines four strategies: write, select, compress, and isolate. It then breaks down the six components of an agent's context that require real-time updates: working memory, session state, long-term memory, tool results, retrieved documents, and ML features. Finally, it stresses the importance of low-latency reads for the agent's read path, arguing that fast reads are essential for making freshness checks affordable on every step and preventing the agent from becoming a bottleneck.

7/14/2026

This post explains agent interoperability, detailing the limitations of current protocols (MCP, A2A) in managing shared state. It positions Redis as a solution for the shared-state layer required for multi-framework agent systems, highlighting its capabilities in vector search, semantic caching, and pub/sub for patterns like blackboard architecture and layered memory. It also mentions native integrations with LangGraph, AutoGen, Cognee, and MCP.

7/11/2026

Introduces dynamic chunking for RAG, contrasting it with fixed-size chunking. Details content-adaptive and query-adaptive strategies including semantic, late, hierarchical, proposition-based, and agentic chunking. Explains real-time retrieval improvements via re-ranking, adaptive routing, query rewriting, and context assembly. Positions Redis Iris as a foundational context engine.

7/7/2026

This post introduces multi-step AI agents, explaining their think-act-observe loop and the challenges they face in production, particularly concerning state management and context handling. It details how these agents break down goals into sequential actions, manage accumulating context, and deal with error propagation. The post highlights the need for a robust data layer to support short-term memory, long-term memory, and operational state, and introduces Redis Iris as a solution for providing agents with fresh context and long-term memory. It discusses specific failure modes like context poisoning, distraction, confusion, and clash, and emphasizes that structural fixes in the data layer are crucial for agent reliability.

7/4/2026

This post introduces agentic AI testing, highlighting its differences from traditional model testing due to non-determinism, statefulness, and error propagation through tool use and multi-agent interactions. It categorizes agentic testing methods into tool-level, trajectory, end-to-end, simulation-based, LLM-as-judge, adversarial, regression, and failure analysis. It also details essential observability instrumentation for agentic systems, including tracing agent invocations, LLM calls, tool executions, and memory/retrieval operations, with considerations for PII and multi-agent tracing. Finally, it outlines infrastructure considerations for production agent testing, emphasizing concurrency and statefulness.

7/2/2026

This post defines 'semantic overload' as a failure mode in AI agents where excessive or contradictory semantic content degrades performance. It details why vector search alone is insufficient, citing limitations in handling multi-hop questions, exact matches, negation, and temporal recency. The post introduces four approaches to mitigate semantic overload: hybrid search, re-ranking with cross-encoders, knowledge graphs/GraphRAG, and structured, graph-based memory. It argues for a unified context layer, exemplified by Redis Iris, to integrate retrieval, memory, and freshness for improved agent accuracy and reduced latency.

7/1/2026

This post elaborates on the concept of AI agent memory, specifically focusing on implementing short-term and long-term memory mechanisms using Redis. It details strategies for storing and retrieving memories, including summarization, vectorization, and extraction, and discusses memory decay management. It positions Redis Iris as a solution for unifying these memory patterns.

7/1/2026

This post introduces LLM router architecture, detailing three primary routing strategies: rule-based, semantic, and predictive. It outlines production-ready architectural patterns, emphasizing low-latency request handling, robust failure management (circuit breakers, multi-provider failover), and the critical role of semantic caching before routing. The post positions vector search and a unified context layer (like Redis Iris) as essential for enabling both semantic caching and semantic routing, thereby optimizing LLM usage for cost and reliability.

7/1/2026

This post elaborates on the challenges of context window limitations in LLMs, introducing the concepts of 'context rot' and specific failure modes (poisoning, distraction, confusion, clash). It details the sources of low-signal tokens and proposes techniques for improving token efficiency, including reranking, hybrid search with metadata filtering, context compression, and strategic pruning/ordering of context. It also introduces semantic caching as a method to reduce token expenditure.

6/24/2026

Introduces knowledge graph RAG as a method to improve AI agent retrieval by modeling data as entities and relationships, enabling multi-hop retrieval. Contrasts knowledge graph RAG with vector RAG, detailing a four-step pattern (data extraction, query entity linking, graph traversal, response generation). Highlights limitations of vector search for relationship-path queries, whole-corpus questions, and dense domains. Discusses the freshness problem in knowledge graphs due to batch indexing and proposes incremental, near-real-time updates. Outlines a tiered agent memory stack (short-term working memory, long-term memory, entity relationships/multi-hop queries) for structured retrieval.

6/23/2026

This post clarifies the distinction between prompt engineering and context engineering, positioning context engineering as the broader discipline responsible for managing all information within an LLM's context window. It argues that production AI agent failures are often due to stale or fragmented context, which is an infrastructure problem, rather than prompt issues. The post introduces the concept of a 'context engine' as the infrastructure layer that assembles this context at runtime, highlighting Redis Iris as a solution that integrates retrieval, memory, and caching capabilities. It details how LangCache provides semantic caching to reduce LLM calls and latency, and how Agent Memory manages memory consolidation. The post emphasizes that context assembly at runtime, drawing from live data sources, is crucial for agent performance and addresses concurrency, consistency, and durability challenges.

6/17/2026

This post clarifies the distinction between context windows and agent memory, arguing that larger context windows do not inherently solve agent memory issues due to 'lost in the middle' effects and performance degradation with length. It highlights the cost implications of stateless LLM APIs and the quadratic growth of cumulative costs with conversation length. The post introduces semantic caching as a cost-saving measure and emphasizes the need for a persistent memory layer outside the model for long-term continuity. It advocates for selective retrieval over dumping full history to improve efficiency and reduce latency, framing agent memory as an infrastructure problem.

6/15/2026

This post provides a practical 6-step playbook for addressing AI agent context limits. It details techniques for trimming tool outputs, summarizing conversation history, migrating durable state to external storage (highlighting Redis as a suitable solution), implementing on-demand retrieval, and isolating context-heavy subtasks. It emphasizes that context limits are an architectural problem, not solely a model prompting issue, and that a fast external store is crucial for production agents.

6/14/2026

This post introduces five principles for production AI context engineering: 1. Treat the whole run as the cost unit, not the turn, highlighting semantic caching (Redis LangCache) for cost savings. 2. Filter tool outputs at ingestion to prevent bloat and context poisoning. 3. Keep static and dynamic context in separate layers, leveraging unified data layers like Redis Iris for coherence. 4. Treat retrieval as a budget decision, not a fetch-everything default, discussing the 'lost in the middle' problem and the role of re-ranking. 5. Acknowledge that context failures can be invisible to standard evaluations. It also mentions Redis's capabilities in vector search (FLAT, HNSW, SVS-VAMANA indexes) and hybrid retrieval.

6/11/2026

Introduces RedisVL MCP, a new component that exposes Redis Search indexes as governed retrieval surfaces for AI agents. It defines two tools: `search-records` for querying and `upsert-records` for writing data, with features like schema introspection, configurable embedding, structured filtering, and runtime governance knobs (limits, timeouts, read-only mode). It also details server configuration, client integration examples, and error contract.

6/10/2026

This post deepens the understanding of context engineering for AI by framing the AI context window as a budget. It details the dual costs of tokens: financial and performance degradation, explaining phenomena like the 'lost in the middle' problem and diminishing returns. It provides a granular breakdown of context window components (system prompt, history, retrieved data, tool output) and introduces strategies for efficient context management, including sliding window, lazy context loading, retrieval-on-demand, and external memory stores. It specifically highlights Redis Iris's capabilities (Context Retriever, Agent Memory, LangCache) for enabling these strategies with low-latency retrieval. Furthermore, it introduces semantic caching as a technique to reduce redundant LLM calls based on intent similarity, contrasting it with exact-match caching and discussing threshold tuning.

6/9/2026

This post details the six categories of input that compete for an AI agent's finite context window at each inference step: system instructions, goal specification, conversation memory (short-term and long-term), retrieved external knowledge, tool definitions, and execution state. It explains the engineering challenge of curating these inputs for optimal signal-to-noise ratio and discusses the infrastructure requirements for fast retrieval of these diverse data types, including key-value reads, vector/full-text search, and session state management. It highlights the latency budget constraints and the need for a memory-first architecture like Redis Iris to serve these access patterns efficiently.

6/3/2026

This post deepens the understanding of context engineering for AI by detailing the limitations of AI reasoning models in production environments. It identifies five specific failure modes of reasoning models (cost/latency, hallucination, overthinking, diminishing returns, untrustworthy traces) and reinforces the argument that context quality is the primary bottleneck. It further elaborates on how the data layer, particularly retrieval architecture and data freshness, directly impacts reasoning quality, citing an example where a structured retrieval approach achieved significantly higher accuracy than a flat one. The post explicitly positions Redis and Redis Iris as solutions for building robust AI agents by providing fast, relevant, and fresh context, including vector search, semantic caching, and agent memory.