--- Title: How agents work alwaysopen: false categories: - docs - develop - ai description: Learn how AI agents work and why Redis is the perfect foundation for building intelligent systems linkTitle: How agents work weight: 10 --- ## How AI agents work AI agents are autonomous systems that go far beyond simple chatbots. They combine large language models (LLMs) with external tools, memory, and planning capabilities to accomplish complex tasks. **Key differences from chatbots:** - Maintain state across multiple conversations - Reason through problems step-by-step - Take actions in the real world - Learn and adapt from interactions ### Core agent architecture {{< image filename="/images/ai_agent/ai-agent-architecture-diagram.svg" alt="AI agent architecture" >}} ### The agent processing cycle Every user interaction follows a 6-step cycle that makes agents intelligent: {{< image filename="/images/ai_agent/simple-processing-cycle.svg" alt="AI agent processing cycle" >}} Why this cycle matters: - Maintains context across multiple conversations - Learns from experience to improve future responses - Handles complex tasks that require multiple steps - Recovers from failures and adapts plans in real-time > Example: When you ask "Book me a flight to Paris and find a hotel," the agent breaks this into separate tasks, remembers your travel preferences, searches for options, and coordinates the booking process. ## Why Redis powers AI agents Redis is the **ideal foundation** for AI agents because it excels at the three things agents need most: **speed**, **memory**, and **search**. ### Redis powers every part of your agent

Planner

Stores workflow templates and agent plans as Hashes or JSON. Enables complex multi-step reasoning.

Retriever

Vector Search finds semantically similar documents instantly. Supports hybrid search for better results.

Executor

Stores conversation history, user preferences, and intermediate results. Maintains state across workflows.

### Key advantages **Ultra-fast response times** - Sub-millisecond data access keeps conversations flowing naturally - In-memory processing eliminates disk I/O bottlenecks - Optimized data structures for different use cases **Built-in vector search** - Native vector indexing with HNSW, FLAT, and SVS-VAMANA algorithms - SVS-VAMANA leverages Intel hardware acceleration for enhanced performance - Hybrid search combining vector similarity with metadata filtering - Real-time updates without index rebuilds - [Learn more about Redis Vector Search →]({{< relref "/develop/ai/search-and-query/vectors/" >}}) **Agent memory** - **Short-term**: Conversation context and session state - **Long-term**: User preferences and learned patterns - Flexible data structures (Hashes, Lists, Streams, JSON) for different memory types - [Explore Redis data structures →](/develop/data-types/) ## Types of agents you can build

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 →](../)

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 →](../)

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

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

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

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

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

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

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

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
## Agent architecture patterns ### Single-agent systems Simple agents that handle all tasks within one system: - Easier to develop and maintain - Good for focused use cases - All logic contained in one place - Suitable for most applications ### Multi-agent systems Multiple specialized agents working together: - Each agent handles specific domains or tasks - agents can communicate and coordinate - More complex but more scalable - Good for enterprise applications ### Hierarchical agents Agents organized in layers with different responsibilities: - High-level agents handle planning and coordination - Low-level agents execute specific tasks - Clear separation of concerns - Easier to debug and maintain ## Redis data structures for agent memory Understanding how to map agent memory needs to Redis data structures is crucial for building efficient agents: ### Redis streams for conversation history - Use case: Ordered conversation logs with timestamps and metadata - Key benefits: Automatic ordering, range queries, consumer groups, guaranteed delivery - Implementation: Store user/agent message pairs with rich contextual metadata - Retention: Use XTRIM for automatic cleanup based on age or count limits - [Learn about Redis Streams →](/develop/data-types/streams/) ### Redis Hashes for User Profiles - Use case: Structured user data with frequent partial updates and atomic operations - Key benefits: Memory efficient field-level operations, atomic updates, O(1) field access - Implementation: Multi-layered profile system with preferences, behavior patterns, and learned data - Scaling: Hash tags for cluster distribution, field expiration for data lifecycle management - [Learn about Redis Hashes →](/develop/data-types/hashes/) ### Redis JSON for Complex State - Use case: Nested data structures, complex agent workflows, hierarchical configurations - Key benefits: JSONPath queries, atomic nested updates, schema validation, efficient storage - Implementation: Multi-step task orchestration, complex decision trees, dynamic configurations - Querying: Advanced JSONPath expressions for complex data retrieval and manipulation - [Learn about Redis JSON →](/develop/data-types/json/) ### Redis Sets for Relationships and Tags - Use case: Entity relationships, user interest tracking, session management, feature flags - Key benefits: O(1) membership testing, efficient set operations, automatic deduplication - Implementation: Complex relationship modeling, real-time recommendation engines, access control - Operations: Union, intersection, difference for advanced analytics and personalization - [Learn about Redis Sets →](/develop/data-types/sets/) ### Redis Vector Sets for Semantic Search - Use case: Embedding storage, similarity search, semantic retrieval, content recommendations - Key benefits: High-performance vector similarity search, multiple distance metrics, real-time indexing - Implementation: RAG systems, semantic memory, content discovery, personalized recommendations - Queries: K-nearest neighbor search, range queries, hybrid filtering with metadata - [Learn about Redis Vector Sets →]({{< relref "/develop/data-types/vector-sets" >}}) ### Redis Sorted Sets for Rankings and Priorities - Use case: Dynamic scoring systems, priority queues, leaderboards, time-series data - Key benefits: O(log N) insertions, range queries by score/rank, atomic score updates - Implementation: Real-time recommendation scoring, task prioritization, performance analytics - Queries: Range by score, rank, lexicographical order, and complex aggregations - [Learn about Redis Sorted Sets →](/develop/data-types/sorted-sets/) ## Reliability features Production-ready agents include built-in reliability features: ### Error handling - Gracefully handle API failures and unexpected inputs - Provide helpful error messages when things go wrong - Continue functioning even when some components fail ### Retry logic - Automatically retry failed operations with exponential backoff - Handle temporary network issues and rate limiting - Ensure important operations complete successfully ### Logging and monitoring - Track what your agent does for debugging and improvement - Monitor performance metrics like response times - Log errors and unusual behavior for investigation ### Performance optimization - Cache frequently accessed information - Use efficient data structures for fast retrieval - Scale resources based on demand ## Production deployment considerations ### Monitoring and Observability - Agent performance metrics: Response times, success rates, user satisfaction - Redis metrics: Memory usage, connection counts, operation latencies - LLM usage tracking: Token consumption, API costs, rate limiting - Business metrics: Task completion rates, user engagement, conversion ### Security and Privacy - Data encryption: Encrypt sensitive data at rest and in transit - Access controls: Implement proper authentication and authorization - Data retention: Automatic cleanup of personal data per regulations - Audit logging: Track all data access and modifications ### Scaling Strategies - Horizontal scaling: Multiple agent instances with shared Redis state - Load balancing: Distribute requests across agent instances - Redis clustering: Scale data storage across multiple nodes - Caching layers: CDN for static content, Redis for dynamic data - [Learn about Redis scaling →]({{< relref "/operate/rs/clusters" >}}) ### Cost Optimization - LLM cost management: Use appropriate models for different tasks - 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 - [Redis performance optimization →]({{< relref "/operate/rs/databases/memory-performance" >}}) --- ## Key takeaways
**What makes agents different:** Agents maintain memory, plan multi-step tasks, and learn from interactions—unlike simple chatbots. **Why Redis is perfect:** Sub-millisecond data access, built-in vector search, and flexible data structures designed for agent workflows. **What you can build:** Conversational assistants, recommendation engines, and complex multi-agent systems.
## Next steps Ready to build your AI agent with Redis? **Get started:** - [Use the agent builder]({{< relref "/develop/ai/agent-builder" >}}) to generate your code and get started - [Redis quick start guide]({{< relref "/develop/get-started" >}}) for setting up Redis **Learn more:** - [Redis Vector Search documentation]({{< relref "develop/ai/search-and-query/vectors" >}}) - [RedisVL Python library]({{< relref "develop/clients/redis-vl" >}}) for vector operations and AI workflows - [Redis data structures guide](/develop/data-types/) - [Redis client libraries]({{< relref "/develop/clients" >}}) for your programming language **Deploy and scale:** - [Redis Cloud]({{< relref "/operate/rc" >}}) for managed Redis hosting - [Redis Software]({{< relref "/operate/rs" >}}) for on-premises deployment - [Performance optimization]({{< relref "/operate/rs/databases/memory-performance" >}}) best practices