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Context Engineering for AI

36 posts · Jun 2026 – Oct 2026

What this trail covers

Redis Iris continues to evolve as a real-time context engine for AI agents, integrating memory, retrieval, operational data, and semantic caching. It provides a unified platform for short-term recall, long-term retrieval, and operational state management, with features like Redis Agent Memory for tiered memory management and Redis LangCache for cost-effective LLM inference. The goal is to consolidate agent data paths, reducing complexity and improving reliability, enabling agents to connect to o

All Redis trails
  1. How we built the Redis Docs MCP for agents | RedisThis post details the development of Redis Docs MCP, a public MCP server for accessing Redis documentation. It covers the MCP tool surface (`search`, `fetch`, `ask`), the rationale behind a keyless, public server, and the architecture involving a single writer and two readers for the docs index. The implementation uses RedisVL for direct index querying by `search` and `fetch`, while `ask` leverages Context Retriever and a language model. The post also discusses design review processes and the importance of stable identifiers for agent retrieval.
  2. Put Redis data and engineering guidance to work in ChatGPT Work | RedisThis post introduces a new Redis Development plugin for ChatGPT Work and Codex, which integrates Redis-specific engineering guidance directly into the AI agent's context. This plugin aims to improve the developer experience by providing real-time assistance for writing, reviewing, and troubleshooting Redis code, including guidance on data structures, connections, Redis Search, semantic caching, clustering, security, observability, and Redis Iris agent memory. The plugin enhances agent capabilities by offering current engineering best practices and newer features like LangCache and Redis Agent Memory, leading to improved pass rates and more direct responses in agent-generated advice.
  3. ReAct Agents Explained: Concepts & Practical UsesThis post explains the ReAct agent pattern, detailing its reasoning-and-acting loop (Thought, Action, Observation, Repeat/Finish). It contrasts ReAct with Plan-and-execute and Reflexion patterns, and describes three tiers of context management: prompt, thread-scoped persistence, and cross-thread long-term memory. The post also discusses practical uses in customer support and coding agents, and analyzes the performance and cost challenges of growing context windows in production, including context poisoning, distraction, confusion, and clash.
  4. Agent Memory as a Moat: How Context CompoundsThis post details the concept of agent memory, differentiating between short-term and long-term memory, and categorizing long-term memory into semantic, episodic, and procedural types. It explains how memory transforms stateless RAG into a learning system through a write-manage-read loop. The post emphasizes the compounding value of accumulated context as a competitive moat and outlines key governance mechanisms: memory scopes/namespaces, retention policies (TTL, adaptive decay, salience-driven retention), and security measures (namespace isolation, RBAC, encryption, audit logs). It also addresses the challenge of fast recall, noting that retrieval latency is a significant factor in memory architectures.
  5. Change Data Capture vs Batch ETL for AI AgentsThis post advocates for Change Data Capture (CDC) over batch ETL for keeping AI agent context fresh. It details log-based CDC (PostgreSQL, MySQL binlog via Debezium) as superior to trigger-based and polling methods due to lower latency, capture of deletes, and minimal impact on write transactions. It highlights the structural staleness of batch ETL, citing examples from Uber and Netflix where stream processing significantly reduced data freshness lag. The post emphasizes that stale context leads to agent errors, liability (Air Canada example), and degraded output quality (context poisoning, version-aware RAG benchmarks). It positions CDC as a critical engineering choice for reliable AI agent operation.
  7. How Redis brings persistent memory to Snowflake Cortex Agents | RedisThis 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. Top vector database alternatives for RAG pipelinesThis 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.
  9. Semantic memory search for AI agentsThis 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.
  10. Connect AI Agents to Data Sources with RedisThis 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.
  11. Multi-Agent Observability: Why One Trace Isn't EnoughThis post introduces the concept of multi-agent observability, detailing the challenges of tracing and correlating actions across multiple agents, shared memory, and external tools. It identifies the 'attribution gap' and 'context fragmentation' as key problems and proposes a shared state layer, potentially leveraging Redis, to improve observability by correlating decision metadata, inter-agent messages, memory operations, retrieval quality, tool interactions, and identity/permission scope.
  12. Context Engineering for AI: What It Is & How to Build ItIntroduces 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.
  13. The 4 Failure Modes of Agent Context in ProductionThis 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.
  14. Token-Budget-Aware LLM Reasoning: Cut Costs in 2026Introduces 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.
  15. Top Reranking Models to Boost RAG Accuracy in 2026This 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.
  16. 5 agent architecture scenarios: assess MCP vs A2AThis 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.
  17. Context Assembly: Building the Prompt the Model SeesThis 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.
  18. Model Context Protocol (MCP) vs. Agent2Agent (A2A): which protocol do you need?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.
  19. RAG Debugging Guide: Fix Retrieval Errors FastThis 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.
  20. Cache Consistency: Strategies to Keep Data FreshThis post details cache consistency strategies, explaining what it means, why caches drift (TTL timing, write ordering, instance races), and comparing classic patterns like cache-aside and write-through. It introduces event-driven synchronization using Redis Keyspace Notifications and Change Data Capture (CDC) as more robust methods to keep data fresh, especially for changes originating outside the application. The post highlights the engineering effort involved in managing ordering, deduplication, and replay logic for these event-driven approaches.
  21. Real-Time Context for AI Agents: Keep Inputs FreshThis 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.
  22. Agent interoperability: a complete explainerThis 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.
  23. Dynamic chunking for RAG: building context infrastructure that adaptsThis post introduces dynamic chunking strategies for RAG to improve context infrastructure. It details the breakdown of fixed-size chunking due to document and query variations, and explores content-adaptive strategies: semantic chunking (splitting by meaning shifts), late chunking (embedding full documents then segmenting), hierarchical chunking (parent-child indexing), proposition-based chunking (atomic factoids), and agentic/query-aware chunking (LLM-driven boundary selection or multi-granularity routing). It also discusses real-time retrieval improvements via re-ranking, adaptive routing, query rewriting (including model racing), and optimal chunk ordering to combat context confusion and rot. Redis Iris is positioned as the underlying vector storage and retrieval layer for these dynamic RAG operations.
  24. Multi-Step AI Agents: What They Are & How They WorkThis post details the mechanics of multi-step AI agents, explaining the think-act-observe loop (ReAct pattern) and the challenges of managing state in production. It highlights how context issues like poisoning, distraction, confusion, and clash can lead to agent failures. The post emphasizes the critical role of the data layer in providing short-term memory, long-term memory, and operational state for reliable agent execution, and how Redis Iris addresses these needs.
  25. Agentic AI Testing: Methods & Best PracticesThis post introduces agentic AI testing methods and best practices, highlighting the unique challenges of testing agents compared to standalone models due to non-determinism, tool use, memory, and multi-agent systems. It proposes a taxonomy of testing methods including tool-level/component testing, trajectory/path evaluation, end-to-end task completion, simulation-based testing, LLM-as-judge, adversarial testing, regression testing, and failure analysis. The post also details essential observability instrumentation for agent traces, focusing on capturing agent invocations, LLM calls, tool executions, and memory/retrieval operations, along with considerations for PII and multi-agent systems. Finally, it discusses infrastructure considerations for production agent testing, emphasizing stateful concurrency, latency goals for task completion, and scaling inference and tool serving.
  26. Semantic overload: why AI agents get facts wrongThis post identifies and explains the problem of 'semantic overload' in AI agents, where an excess of similar but potentially contradictory or outdated information leads to factual errors. It details specific failure modes like context poisoning, distraction, confusion, clash, and context rot. The post critically analyzes the limitations of vector search alone, particularly its inability to reason over explicit relationships between entities, handle temporal blindness, or correctly interpret negation. It also describes the 'relational gap' in current agent memory systems, leading to contradictions, lack of provenance, and scalability issues. Finally, it proposes and discusses four approaches to mitigate semantic overload: hybrid search, re-ranking with cross-encoders, knowledge graphs/GraphRAG, and structured, graph-based memory, emphasizing the need for a unified context layer.
  27. Build AI agents with short-term & long-term memoryThis 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.
  28. LLM router architecture: best practices for 2026This post details the architecture and best practices for building LLM routers, a middleware layer that directs requests to the most suitable LLM based on signals like task complexity, cost, and provider latency. It outlines three routing strategies: rule-based, semantic (using vector embeddings and cosine similarity), and predictive (learning from data to estimate quality and cost). Production architecture patterns are discussed, including keeping the hot path clean by offloading observability work, planning for various failure modes (5xx errors, rate limits, latency increases, partial streaming failures) with circuit breakers and multi-provider failover, and implementing semantic caching before routing to bypass LLM calls for semantically similar queries. The post emphasizes the role of vector search engines in enabling fast semantic caching and routing, and the benefit of a unified context layer like Redis Iris for managing agent memory, live data, and retrieval to optimize LLM router performance and cost.
  29. Token efficiency: getting more signal into the context windowThis post details the challenges of token inefficiency in LLMs, leading to degraded reasoning and specific failure modes (context poisoning, distraction, confusion, clash) due to the 'lost in the middle' effect and attention budget limitations. It explains how low-signal tokens (boilerplate, duplicates, irrelevant tool schemas) consume valuable context window space. The post proposes solutions for high-signal token selection: reranking (using cross-encoders to improve Mean Reciprocal Rank), hybrid search with metadata filtering (combining vector and keyword search), context compression (e.g., LLMLingua for token perplexity scoring), and pruning/ordering of context. It also introduces semantic caching as a method to avoid unnecessary LLM calls by indexing responses with query embeddings.
  30. Knowledge graph RAG: structured retrieval for AI agentsThis post introduces Knowledge Graph RAG as an advancement over traditional vector RAG. It details how modeling data as entities and relationships (nodes and edges) allows for structured, multi-hop retrieval, which is crucial for answering complex agent queries that span multiple documents or facts. The post outlines a four-step pattern for Knowledge Graph RAG (data extraction, query entity linking, graph traversal, response generation) and highlights the limitations of vector search for relationship-path queries, whole-corpus questions, and dense domains. It also addresses the critical 'freshness problem' in knowledge graphs, where stale data can lead to incorrect agent reasoning, and emphasizes the need for near-real-time updates. The post positions structured retrieval as a key component of context engineering for AI agents, complementing short-term and long-term memory tiers.
  31. Context engineering vs prompt engineering: the differenceThis post clarifies the distinction between prompt engineering and context engineering for AI agents. It argues that prompt engineering, while valuable, is insufficient for production agents due to their multi-step nature and dynamic inputs. Context engineering, encompassing the curation and maintenance of all data within the model's context window (instructions, knowledge, tools), is presented as the more critical discipline. The post identifies stale and fragmented context as infrastructure problems, not prompt issues, and positions Redis Iris as a context engine that addresses these challenges through efficient assembly of retrieval, memory, and caching mechanisms. It emphasizes that context assembly at runtime, rather than design time, is key to agent performance and reliability.
  32. Sub-agents: splitting context across specialized AI agentsThis post introduces the concept of sub-agents as a solution to the context window limitations and quality degradation issues faced by single, generalist AI agents. It details various coordination patterns (Orchestrator-worker, Supervisor, Swarm, Router) and highlights the critical role of shared memory and retrieval in maintaining coherence between these specialized agents. The post explains how context poisoning, confusion, clash, and rot can occur without a common ground truth and how layered memory (short-term, long-term, episodic, semantic) combined with vector search and metadata filtering in Redis Iris can mitigate these issues, enabling more robust multi-agent systems.
  33. Why a Bigger Context Window Won't Fix Agent MemoryThis post argues that larger LLM context windows do not inherently solve agent memory issues, as models suffer from 'lost in the middle' effects and quality degradation with length. It differentiates between context windows (per-call input buffer) and agent memory (system for continuity across sessions). The post details the recurring cost of stateless LLM APIs and the benefits of semantic caching with invalidation strategies. It proposes a tiered memory model (short-term, working, long-term) and emphasizes building a persistent memory layer independent of model choice. Selective retrieval is presented as a more efficient pattern than dumping full history, reducing latency and cost. Redis Iris is positioned as a solution for providing both short-term and long-term memory for agents.
  34. AI Agent Context Limit: 6-Step Recovery PlaybookThis post provides a practical playbook for recovering AI agents that hit context limits. It outlines six steps: 1. Trim tool outputs (hard token caps, pagination, dynamic filtering, referencing instead of inlining). 2. Summarize and compact older turns to reduce token count and correct errors, while noting the lossy nature and suggesting external storage for full history. 3. Move durable state out of the context window into external storage (like Redis) as the standard architecture for production agents. 4. Implement on-demand retrieval (JIT) instead of carrying the full history, using RAG pipelines and best practices like budgeting the window and checking relevance. 5. Isolate context-heavy subtasks into scoped subagents running in their own windows. 6. (Implied) Use a fast external store like Redis Iris for efficient retrieval and state management.
  35. 5 Context-Engineering Principles for Production AIThis post introduces five production-focused principles for context engineering in AI agents: 1. Budgeting cost per entire agent run, not per turn, highlighting how cumulative input costs grow and the role of semantic caching (e.g., Redis LangCache) in reducing inference costs. 2. Filtering tool outputs at ingestion to prevent bloat and context poisoning, suggesting strategies like offloading large responses, truncating noisy content, and clearing stale results. 3. Layering static (instructions, persona) and dynamic (retrieved data, history) context, advocating for a unified data layer (e.g., Redis Iris) to manage these distinct layers coherently. 4. Treating retrieval as a budget decision, emphasizing the 'lost in the middle' problem and the damage caused by 'hard negatives,' and detailing Redis's support for various index types (FLAT, HNSW, SVS-VAMANA) and hybrid retrieval. 5. Recognizing that context failures are often invisible to standard evaluations, implying the need for more robust testing methodologies.
  36. AI agent access control: a practical guideThis post details practical strategies for implementing AI agent access control, focusing on enforcing permissions at the retrieval layer within RAG pipelines. It introduces metadata filtering, permission-aware retrieval, and layered policy enforcement as key patterns. The post elaborates on document-level, row-level, and field-level access control, advocating for ABAC and ReBAC models over static RBAC for dynamic agent authorization. It stresses that access controls must live at the data layer, upstream of the LLM, to prevent prompt injection bypasses. The core contribution is a practical guide to preventing data leaks by ensuring agents only access data they are authorized for, based on the user, task, and workflow context, rather than just their authenticated identity.
  37. What MCP Gives Your Agents & What It Leaves to YouThis post clarifies the scope of the Model Context Protocol (MCP), emphasizing that it standardizes tool invocation and wire format but explicitly excludes memory, freshness, and reliability concerns. It details how MCP's stateless design necessitates a robust underlying data layer for these functions. The post highlights Redis's capabilities in providing solutions for agent memory (short-term, long-term, episodic), data freshness (Data Integration, LangCache), and reliability (low-latency retrieval, fast and current data access) to complement MCP's tool connectivity.
  38. Connect Your Redis index to AI agents with RedisVL MCP | RedisIntroduces the RedisVL MCP (Model Context Protocol) server, a specialized tool for exposing Redis Search indexes as governed retrieval surfaces for AI agents. It details the `search-records` and `upsert-records` tools, schema introspection, filtering capabilities, server-side embedding for upserts, runtime governance knobs (limits, timeouts), and a typed error contract. The post also covers installation, transport modes (stdio, SSE, streamable-http), server configuration, client configuration examples for Claude Desktop and Cursor, and a concrete example of an agent query. Future work includes multi-index support.
  39. Quality Context: Why AI Agents Need Better Info, Not MoreThis post details the critical importance of context quality over quantity for AI agent performance. It elaborates on context failure modes (poisoning, distraction, confusion, clash, rot) and explains how transformer attention's quadratic complexity makes every token count. The post emphasizes that retrieval quality is paramount, with precision-focused re-ranking, hybrid search (BM25 + dense retrieval with RRF), metadata filtering, and freshness constraints being key to improving context quality. It highlights that pure vector search can miss exact terminology and pure keyword search can miss semantic meaning, making hybrid search superior. The post also discusses the performance degradation caused by irrelevant or contradictory information in the context window and the U-shaped attention curve effect. Redis Iris is presented as a system that addresses these challenges by providing low-latency, high-quality context to AI agents.
  40. Context window in AI: why every token is a budget decisionThis post elaborates on the concept of AI context windows as a budget, detailing the dual costs of tokens: monetary expense and potential degradation of model reasoning quality. It explains how self-attention mechanisms contribute to the computational cost of larger windows and discusses the 'lost in the middle' problem where models may underutilize information placed in the middle of long contexts. The post breaks down where the context budget is spent (system prompt, tool schemas, conversation history, RAG retrieved chunks, tool call outputs, output buffer) and proposes strategies for keeping context lean. These include sliding windows, lazy context loading, retrieval-on-demand, and external memory stores. It specifically highlights Redis Iris components like Context Retriever and Agent Memory as solutions for managing context efficiently with sub-millisecond response times. Additionally, the post introduces semantic caching as a method to reduce repeated LLM calls by indexing responses based on vector embeddings of input prompts, contrasting it with exact-match caching and discussing the tuning of similarity thresholds.
  41. AI Agent Context: What Goes Into the WindowThis post details the critical role of context engineering in AI agent development, emphasizing that agent performance is limited by the information available in its context window at inference time. It breaks down the six key inputs competing for this finite budget: system instructions, goal specification, conversation memory (short-term and long-term), retrieved external knowledge (RAG), tool definitions, and execution state. The post highlights the infrastructure challenges of assembling these diverse data sources quickly, arguing that low-latency access to key-value stores, vector search, and session state is paramount. It positions Redis Iris as a solution for serving these varied access patterns with sub-millisecond performance, enabling agents to operate efficiently and make informed decisions.
  42. Redis Data Integration in Redis Cloud is now GA in AWS | RedisThis post announces the general availability of Redis Data Integration (RDI) in Redis Cloud on AWS. It details RDI's capabilities in moving operational and analytical data into Redis in near real-time, including initial bulk hydration and ongoing CDC. New features highlighted are improved CDC latency and throughput, faster full hydration, enhanced pipeline management with better validation and error reporting, advanced configuration options, snapshot SQL support, and new source support for Snowflake (preview) and MongoDB. The post also emphasizes RDI's role as a foundational component for AI agents, enabling them to access live context from source systems.
  43. Why AI Misses Business Context & How Teams Fix ItThis post details common failure modes in enterprise AI due to a lack of current business context, arguing that the surrounding infrastructure (the context layer) is the primary bottleneck, not the LLM itself. It explains how context gaps lead to AI systems producing unusable output in production environments across various applications like customer support, sales, internal knowledge, and operations. The post emphasizes the need for a context layer that separates systems of record from query layers, retrieves the right material (not just more), and serves it with low latency. It positions Redis Iris as a solution that provides this context layer through components like Redis Context Retriever, Redis Agent Memory, Redis Data Integration, Redis LangCache, and Redis Search, enabling AI agents to reason over fresh, relevant, and fast-changing business state.
  44. AI Reasoning Explained: Why Context MattersThis post explains the concept of AI reasoning in LLMs, highlighting its cost and latency implications, and its limitations in preventing hallucination and overthinking. It argues that context quality, not model intelligence, is the primary bottleneck for production AI reliability. The post emphasizes the role of the data layer, specifically retrieval architecture and data freshness, in determining reasoning quality. It positions Redis Iris as a solution for building agents that remember by providing fresh context and long-term memory, detailing its components (Redis Context Retriever, Redis Agent Memory, Redis Data Integration, Redis LangCache) and their contribution to efficient context management for AI agents.