97% believe in it. Only 4% have built for it. New research: State of context engineering.
REDIS IRIS
Unreliable agents fail in production. Redis Iris is a unified, real-time context engine that delivers fresh, relevant context so agents perform at scale.
Agents fail because data spread across countless systems is fragmented, stale, slow, and difficult to navigate. The results are inaccurate answers, slow performance, and frustrated users. Agents are missing the key context layer that makes data AI-ready.
Agents rely on connections to deliver the best performance. Data has to work as a connected system agents can explore, not just query.
Latency has a snowball effect. The more time it takes an agent to complete a step, the bigger the risk it will fall apart under real-world workloads.
Data never sits still. Apps change state, CRMs update, and events don't stop. Agents have to trust that the context they have is accurate up to the instant.
Context should get more relevant, personalized, and informed by past interactions. That means agents must have memory, learning, and durable state built in.
Keeps operational state fresh in Redis, so agents act on current business context instead of stale exports, cron jobs, or brittle data pulls.
Gives agents a navigable path through business entities like customers, orders, and tickets, so they can reason over context instead of guessing across tools.
Lets context compound across sessions, channels, and agents, so every interaction can build on what happened before.
Keeps repeated and semantically similar LLM work fast by serving trusted responses from Redis inside the agent’s latency budget.
Give agents clean, schema-first paths through all business data, including customers, orders, tickets, and more. Agents spend less time guessing and more time reliably getting the right answers.
Ground agents in what's actually happening. Pull fresh operational data from your systems of record straight into Redis, at the speed of Redis, so agents act on up-to-the instant business state in one runtime path.
Deliver working memory and long-term recall in one place. Keep current agent conversations tight with active context, while persisting vital long-term pieces like user preferences, past decisions, and more across sessions.
Redis Search pulls and filters live operational context at Redis speed. LangCache cuts the waste, caching semantically similar prompts and responses so agents don't ask the same questions twice.
Our context engineering maturity model gives a clear picture of where you stand today, what's blocking you from the next stage, and where to invest to close the gaps before your competitors do.
8 questions. 2 minutes. Recommendations for your team.
Memory is core to how agents improve over time, and teams we work with are realizing they need scalable infrastructure behind it. Together, Redis Iris and LangSmith's Context Hub give customers a structured way to manage agent context across environments, connecting live operational data and retrieval into the same system that versions and evolves agent memory over time.
At Character.ai, every millisecond matters. Before Redis, we spent too much time fighting latency and managing complex pipelines. Redis lets us deliver fast, intelligent search that feels instantaneous to our users.
Redis Agent Memory helps us store and reuse context across our coding agents in real time. We use it to capture critical engineering decisions, bug details, and development context as our agents work, so that information is available across the team instead of getting lost between sessions or tools. We also have a service reading from and writing to the same memory while monitoring our API, which lets us detect issues and report bugs into our ticketing system in real time. We’re excited to see what we can build next with Redis Iris.
See how Redis Iris keeps your agents grounded in fresh, relevant context.
