Science & Technology
November 5, 2026
SAN FRANCISCO, Nov. 5, 2026 — Redis has announced the next chapter of Redis Iris, its real-time context engine for AI agents. Redis Iris now offers six customizable capabilities designed to be used together or independently that give agents the memory, context, and messaging they need to work reliably in the real world: Redis Intent Router, Redis Agent Playbooks, Redis Agent Memory, Redis Context Gateway, Redis Lens, and Redis Agent Mesh.
AI models and infrastructure are now good enough for the enterprise, yet few teams are seeing repeatable, production-level success with agents. Agents often lack the right context, at the right time, remembered the right way. Those that do deliver value often do so inefficiently, with over-stuffed prompts, redundant LLM calls, and brittle custom integrations that cost more in tokens and time than the value they return.
“Agents need the same foundations as apps and microservices: live app data, runtime state and service communication,” said Rowan Trollope, CEO of Redis. “They also need new capabilities like memory, context management and routing. Redis already sits in the hot path as the leading runtime database for apps. Developers shouldn’t need five infrastructure products to make agents work. Iris brings those primitives together in one place so developers can build agents that work in production.”
Iris delivers production-ready context, memory, and messaging from day one, while letting teams customize each capability to their agents. The release includes:
- Intent Router (GA) matches the meaning of a request rather than its wording and sends it to the right destination, whether a remembered FAQ, an agent skill, a human, or an LLM. It also supports agent and model routing, guardrails, and “fuzzy” if-statements, so decisions are not limited to true or false.
- Agent Playbooks (GA) lets teams define which answers are stable and should be reused, and which require current data and a fresh run of a known procedure. Playbooks become more thorough and accurate the more they run, reducing resolution times and token spend.
- Agent Memory (GA) builds memories from user actions as well as documents and other semi-structured data through MemoryETL, organizes them in hierarchies for finer-grained control, and provides inspection, audit, and provenance. It can also forget what it should not keep, such as personally identifiable information.
- Context Gateway (GA) gives agents a single, governed point of entry to connected context sources. Teams can bring their own MCP servers, cache which tools solve a given problem to reduce token use, and build reusable results for consistency across sessions.
- Lens (beta) makes agent behavior traceable. Every outcome can be traced back to the skill, route, or FAQ that served it, the memory it learned from, and the source session, providing auditable evidence alongside visibility into task success, tokens per task, and latency.
- Agent Mesh (beta) lets agents communicate over ephemeral streams and automatically buffers and reconnects when a connection drops. Durable SSE Streams keep responses flowing through interruptions, and A2A Comms give agents a reliable channel for delegation and handoffs.
Together, the capabilities help ensure agents remember the right things, answer the right way, and are transparent in every action, while delivering more outcomes per token. Because Iris runs on the Redis technology teams already know, organizations can reduce the custom integration work that typically connects retrieval, memory, routing, and messaging.
Built for the Messy Real World
Production agents must handle unpredictable human inputs, multiple channels, and handoffs between agents, any of which can become a point of failure. Iris is built around four principles: context should be navigable rather than siloed, memory should be adaptive and selective, context should be efficient to keep costs in check, and context should compound as agents learn from past interactions.
Since Redis Iris first launched in May 2026, customers have been putting it to work in production.
“Everything you see in Paradigm is stored in Redis. It is the memory and orchestration layer that lets our agents work in real time, at a scale that wasn’t possible before,” said June Lee, CTO, Paradigm.
Redis Iris is available today. Intent Router, Agent Playbooks, Agent Memory, and Context Gateway are generally available; Lens and Agent Mesh are in beta. To learn more, visit redis.io/iris.
Redis will host a live first look at the release with the engineering team behind it on Nov. 12 (Americas) and Nov. 17 (EMEA). Registration is available at redis.io/resources/videos/redis-iris-full-spectrum.
Redis is the world’s fastest data platform. From its openolutions, Redis has helped more than 10,000 customers build, scale, and deploy the apps our world runs on. With cloud and on-prem databases for caching, GenAI, and more, Redis helps digital businesses set a new standard for app speed
Located in San Francisco, Austin, London, Tel Aviv and Singapore, Redis is internationally recognized as the leader in building fast apps fast. Learn more at redis.io.
The Big Data Inside Amazon’s New Fire Phone
The new Fire phone that Amazon launched this week looks like your ordinary black smartphone,…
Three Reasons to be Scared of the Internet of Things
We know the Internet of Things forecasts: 50 billion connected devices by 2020. Apparently, there’s…
Wanted: Intelligent Middleware That Simplifies Big Data Analytics
We’ve seen tremendous technological innovation in the data analytics space over the past 10 years….
GPUs Tackle Massive Data of the Hive Mind
LIVE from GTC12 — The flock of birds that weaves seamlessly through the sky, propelled…
Why Hadoop on IBM Power
In the quest to achieve data-driven insight, Hadoop running on Intel X86-based processors has emerged…
Datanami’s Leverage Big Data Summit Wraps Up
Dialog and networking were on the Datanami agenda this week as we kicked off our…
CrowdStrike and Dynatrace Want Security Teams to Search Data Where It Already Lives
CrowdStrike and Dynatrace have announced three new integrations aimed at helping companies cut the cost…
Who Controls Enterprise AI Agents? Data Platform Vendors Race to Provide the Answer
AI agents are everywhere. Every other news story is related somehow to AI agents. As…
The Token Trap: Why AI Costs Spiral at Scale
Most enterprise AI teams discover the token cost problem somewhere between the demo and production,…
Hugging Face Launches ProtoStar to Tackle Science’s Data Problem
There’s a lot of scientific data out there – and universities, government agencies, and research…
Dataiku Headless Tackles a Growing Problem With AI Coding Agents
Getting an AI coding assistant to write a data pipeline is becoming easier. No surprises…
Oracle’s New Lakehouse Tools Target a Blind Spot in Enterprise AI
Giving AI agents access to company data is one thing. Making sure they understand what…
