diff --git a/content/develop/ai/agent-builder/agent-concepts.md b/content/develop/ai/agent-builder/agent-concepts.md index 97e9009832..164aea2b27 100644 --- a/content/develop/ai/agent-builder/agent-concepts.md +++ b/content/develop/ai/agent-builder/agent-concepts.md @@ -82,144 +82,125 @@ Redis is the **ideal foundation** for AI agents because it excels at the three t - **Managed option**: The [Redis Iris Context Engine](/content/develop/ai/context-engine/agent-memory/_index.md) provides short-term (session) and long-term memory as a managed service — with semantic long-term search — so you don't have to build the vector index and storage yourself - [Explore Redis data structures →](/content/develop/data-types/_index.md) +### Managed agent context with Redis Iris + +You can build each part of an agent's context layer yourself from Redis data structures, or use the [Redis Iris context engine](/content/develop/ai/context-engine/_index.md) services as a pre-built solution. Each service runs fully managed on Redis Cloud, or self-managed on your own infrastructure, and has a REST API. + +| Agent need | Build it yourself with Redis | Redis Iris service | +|:--|:--|:--| +| Remember the user across sessions | Streams, Hashes, and vector search for session and long-term memory | [Agent Memory](/content/develop/ai/context-engine/agent-memory/_index.md) stores session events, summarizes long sessions, and extracts long-term memories in the background. | +| Avoid repeat LLM calls | Vector search plus your own cache logic | [LangCache](/content/develop/ai/context-engine/langcache/_index.md) returns a cached response when a new prompt is semantically similar to a cached one. | +| Query business data safely | Hand-written tools or generated queries per agent | [Context Retriever](/content/develop/ai/context-engine/context-retriever/_index.md) generates tools from a data model you define once. Agents call them over MCP (Model Context Protocol), and agent keys limit what each agent can reach. | +| Keep that data current | Your own sync jobs from the source database | [Data Integration](/content/develop/ai/context-engine/data-integration/_index.md) streams changes from relational databases into Redis within seconds. | + +LangCache, Agent Memory, and Context Retriever are currently in preview. To see where each service fits in a single request, see [how a request flows through Redis Iris](/content/develop/ai/context-engine/concepts/request-flow.md). + ## Types of agents you can build
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Conversational assistants

Build chatbots and virtual assistants that: - Maintain natural conversations with context and memory -- Handle multiple topics within a single conversation - Provide personalized responses based on user history -- Escalate to human agents when needed [Build a conversational agent →](../)
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Recommendation engines

Create intelligent recommendation systems that: - Learn from user behavior and preferences - Provide real-time personalized suggestions -- Handle both explicit feedback (ratings) and implicit signals (clicks, time spent) -- Scale to millions of users and items [Build a recommendation agent →](../)
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Knowledge assistants (RAG)

Build retrieval-augmented generation agents that: - Ingest documents and answer questions with citations - Combine vector search with semantic caching for fast, grounded responses -- Reduce hallucinations by retrieving relevant context before generating answers -- Scale to large document collections [Build a knowledge assistant →](../)
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Redis Iris conversational assistants

Build conversational agents backed by managed Redis Iris Agent Memory that: - Get session and long-term memory without building a vector index -- Persist user preferences and context across conversations - Extract durable memories automatically in the background -- Run on Redis Cloud as a fully managed service [Build a Redis Iris agent →](../)
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Task automation agents

Automate complex workflows and business processes: - Execute multi-step tasks with decision-making - Integrate with APIs and external systems -- Handle error recovery and retry logic -- Monitor and report on task completion
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Data analysis agents

Process and analyze large datasets intelligently: - Perform statistical analysis and pattern recognition -- Generate insights and reports automatically - Handle real-time data streams -- Create visualizations and dashboards -
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Content generation agents

- -Create and manage content at scale: -- Generate articles, summaries, and documentation -- Adapt content for different audiences and formats -- Maintain brand voice and style consistency -- Handle content moderation and quality control
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Customer support agents

Provide intelligent customer service: - Answer questions using knowledge bases - Route complex issues to human agents -- Track customer satisfaction and feedback -- Learn from interactions to improve responses
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Research and retrieval agents

Find and synthesize information from multiple sources: - Search across documents, databases, and web content - Summarize findings and extract key insights -- Fact-check and verify information accuracy -- Maintain up-to-date knowledge repositories
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Monitoring and alerting agents

Watch systems and notify when action is needed: -- Monitor application performance and health - Detect anomalies and security threats - Send intelligent alerts with context -- Suggest remediation actions
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Personal productivity agents

Help users manage tasks and information: - Schedule meetings and manage calendars - Organize and prioritize tasks -- Provide reminders and follow-ups -- Learn user preferences and habits
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Trading and financial agents

Make intelligent financial decisions: - Analyze market data and trends - Execute trades based on predefined strategies -- Manage risk and portfolio optimization -- Generate financial reports and insights
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Content generation agents

+ +Create and manage content at scale: +- Generate articles, summaries, and documentation +- Adapt content for different audiences and formats
@@ -329,6 +310,7 @@ Production-ready agents include built-in reliability features: ### Performance optimization - Cache frequently accessed information +- Cache LLM responses semantically, so paraphrased questions skip the model call. [LangCache](/content/develop/ai/context-engine/langcache/_index.md) does this as a managed service - Use efficient data structures for fast retrieval - Scale resources based on demand @@ -345,7 +327,9 @@ Production-ready agents include built-in reliability features: - Data encryption: Encrypt sensitive data at rest and in transit - Access controls: Implement proper authentication and authorization +- Governed data access: Give agents defined tools instead of direct database access. [Context Retriever](/content/develop/ai/context-engine/context-retriever/concepts.md) generates these tools and uses access tags to filter what each agent can see - Data retention: Automatic cleanup of personal data per regulations +- Sensitive data in memory: Keep information such as payment card numbers out of long-term memory. Agent Memory [sensitive-data exclusions](/content/operate/iris/agent-memory/create-service.md#sensitive-data-exclusions) guide automatic extraction away from it - Audit logging: Track all data access and modifications - Memory integrity: Validate content before it is written to agent memory and verify protected records on read. Memory written from tool results, web pages, or other agents can carry instructions that are replayed into later prompts ([OWASP Top 10 for Agentic Applications, ASI06](https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/); [MITRE ATLAS AML.T0080.000](https://atlas.mitre.org/techniques/AML.T0080.000)). [OWASP Agent Memory Guard](https://github.com/OWASP/www-project-agent-memory-guard) is an open-source guard for this with adapters for LangChain, OpenAI Agents SDK, AutoGen, CrewAI, and mem0 @@ -359,7 +343,7 @@ Production-ready agents include built-in reliability features: ### Cost Optimization -- LLM cost management: Use appropriate models for different tasks +- LLM cost management: Use appropriate models for different tasks, and serve repeat questions from a semantic cache such as [LangCache](/content/develop/ai/context-engine/langcache/_index.md) - Redis memory optimization: Efficient data structures and TTL policies - API rate limiting: Prevent excessive external API calls - Resource monitoring: Track and optimize compute and storage costs @@ -392,6 +376,8 @@ Ready to build your AI agent with Redis? **Learn more:** - [Redis Iris Context Engine — Agent Memory](/content/develop/ai/context-engine/agent-memory/_index.md) for managed session and long-term agent memory +- [Redis Iris concepts](/content/develop/ai/context-engine/concepts/_index.md) for how memory, caching, and governed data access work together +- Interactive demos for [Agent Memory](/content/develop/ai/context-engine/agent-memory/interactive-demo.md), [LangCache](/content/develop/ai/context-engine/langcache/interactive-demo.md), and [Context Retriever](/content/develop/ai/context-engine/context-retriever/interactive-demo.md) that run in your browser - [Redis Vector Search documentation](/content/develop/ai/search-and-query/vectors/_index.md) - [RedisVL Python library](/content/develop/clients/redis-vl.md) for vector operations and AI workflows - [Redis data structures guide](/content/develop/data-types/_index.md)