How agents work

Learn how AI agents work and why Redis is the perfect foundation for building intelligent systems

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

The agent processing cycle

Every user interaction follows a 6-step cycle that makes agents intelligent:

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

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 →

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 →

Types of agents you can build

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 →

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 →

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 →

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 →

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 →

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 →

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 →

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 →

Key takeaways

Next steps

Ready to build your AI agent with Redis?

Get started:

• Use the agent builder to generate your code and get started
• Redis quick start guide for setting up Redis

Learn more:

• Redis Vector Search documentation
• RedisVL Python library for vector operations and AI workflows
• Redis data structures guide
• Redis client libraries for your programming language

Deploy and scale:

• Redis Cloud for managed Redis hosting
• Redis Enterprise for on-premises deployment
• Performance optimization best practices