Agentic AI system components for production

If you're building AI systems that can actually do things (not just answer questions) you're working with agentic AI. These systems observe what's happening, figure out what to do, execute on it, and get better over time.

The components you choose and how you architect them determine whether your agent handles 10 concurrent users or 10,000. Whether it remembers context across sessions or forgets everything.

Let's break down the essential components of agentic AI systems, how they work together, and what infrastructure decisions actually matter when you're building for production.

What makes an AI system agentic?

Agentic AI systems operate autonomously to achieve goals without step-by-step instructions. Unlike chatbots following scripts, agents perceive their environment, make independent decisions, and adapt based on results. This autonomy combined with goal-directed behavior distinguishes agents from traditional AI systems.

• Autonomy means the system makes decisions independently. You give it a goal, and it figures out the path.
• Goal-directed behavior means the system understands objectives and takes deliberate steps to achieve them. It's actively planning, adapting, and pursuing outcomes.

The difference matters when you're building for production. Scripted workflows break when they encounter unexpected inputs, whereas agentic systems adapt.

The essential components of agentic AI systems

Production agentic systems need five core components working together in continuous loops. Understanding each helps you architect systems that actually scale.

1. Perception module

The perception module is the sensory interface: it ingests raw data from the environment and transforms it into structured context the reasoning engine can use. This layer needs connector infrastructure for diverse data sources (APIs, databases, file systems, streaming data), embedding models that vectorize inputs for semantic understanding, feature extraction that identifies relevant patterns, and context assembly that structures observations for reasoning.

Agents use active perception. They request specific information rather than processing everything. Mid-execution, the system might decide it needs additional data and request it specifically, refining perception based on reasoning requirements.

Redis, for example, handles the real-time data ingestion and vector embeddings for perception at sub-millisecond speeds. Without solid perception, agents make decisions based on incomplete or stale information. That's when things break in ways load tests never predicted.

2. Reasoning and planning engine

The reasoning engine transforms observations into decisions through iterative loops that interleave thinking with doing. Four dominant patterns have emerged:

• ReAct (Reasoning + Acting) interleaves thinking with doing—your agent reasons about what to do, takes an action, observes what happened, then decides the next step. This creates feedback loops where each action informs the next decision.
• Chain-of-Thought generates step-by-step reasoning before producing outputs through a linear path, letting you inspect and debug reasoning traces. However, it's limited for dynamic agents requiring feedback loops.
• Tree-of-Thoughts explores multiple reasoning paths simultaneously by maintaining a tree structure. The system can backtrack to earlier decision points when paths prove unproductive, exploring multiple solution branches before committing.
• Language Agent Tree Search combines these patterns with reinforcement learning methods, storing reasoning errors and reflections in memory to learn from past mistakes across multiple cycles.

For a task like "analyze our Q3 sales performance," your reasoning engine generates explicit reasoning (query available data sources), executes tool operations (database queries by region), observes results, then determines next steps (calculate trends, generate visualizations, synthesize findings). This cycle repeats dynamically based on what the data reveals.

3. Memory systems

LLMs are stateless by design. They process inputs without maintaining persistent memory across interactions. Agentic systems must explicitly manage context and memory to work across multiple steps. Memory architecture becomes one of your most critical design decisions.

Production systems need five memory types: short-term (current conversation), long-term (user preferences across sessions), episodic (specific past events), semantic (factual knowledge bases), and procedural (learned behaviors and tools). Each serves different functions and requires different infrastructure.

• Short-term memory handles immediate context—the current conversation, recent actions, working state. This maps to your LLM's context window, but you need strategies for pruning and summarizing when approaching token limits. Redis works well here because you need sub-millisecond access to conversational context.
• Long-term memory persists information across sessions. Episodic memory stores specific events in time order: when did this user last contact support? What was the resolution? Semantic memory stores factual knowledge independent of specific episodes, using vector embeddings for similarity search. Procedural memory stores skills and learned behaviors through fine-tuned models and tool definitions.

Your infrastructure choices determine agent performance more than model selection. You'll spend more time optimizing what information gets surfaced when than tweaking prompts. Effective systems implement semantic caching and policy-based compaction to ensure agents operate with high-quality, relevant information.

Redis provides all five memory types in one platform. Short-term memory in your context window, long-term in Redis Cloud storage, semantic search with Redis Vector Sets, and sub-millisecond retrieval across all of them.

4. Action and execution module

The action module bridges reasoning to real-world interaction. This is where your agent actually does things. Function calling is the technical foundation. The LLM analyzes requests and generates structured JSON specifying which function to call with what parameters, then the runtime executes it. Agents differ from traditional workflows by making dynamic decisions based on intermediate results, iteratively refining action sequences rather than following predetermined paths.

Each tool needs formal definitions: name, natural language description, parameter types, required fields. Tool quality determines success more than prompt sophistication.

Common orchestration patterns include sequential (one step after another), concurrent (parallel execution), group chat (agents collaborate), handoff (switching between specialists), and magentic (master coordinator). Pattern selection depends on task requirements. Use sequential for dependent steps, concurrent for independent parallel work.

Multi-agent workflows require reliable message queuing and state management. Tools like Redis Streams handle this coordination layer, ensuring that when production agents inevitably fail, the architecture supports recovery rather than catastrophic breakdown.

5. Feedback loops

Agentic systems learn through continuous feedback loops. Four key strategies drive this:

• Reflection patterns implement self-evaluation where agents assess their outputs, identify gaps, and iteratively refine through structured feedback loops without human supervision.
• Experience replay stores state-action-reward sequences in memory buffers. Your agent samples from these experiences to improve learning stability and data efficiency.
• Tool-driven feedback loops integrate real-world execution outcomes back into agent reasoning. When a tool invocation returns results, this environmental feedback informs subsequent decision-making steps.
• Multi-agent collaboration lets specialized agents build consensus through shared problem-solving, progressively adapting strategies based on intermediate results and learning from identified failure patterns.

These strategies compound over time. An agent that reflects on its failures, replays successful patterns, and incorporates real-world feedback will outperform a static system within weeks of deployment.

How these components work together

In production, these components integrate through event-driven architectures with streaming data flows. The entire loop operates continuously. Agents perceive, reason, act, observe, and repeat until goals are achieved.

Your events enter through streaming endpoints supporting any protocol. Memory databases assemble working context across short-term and long-term layers. Vector databases perform semantic search to identify relevant knowledge. Context pruning automatically trims information to fit token limits while preserving critical information.

Reasoning processes use the four dominant patterns (ReAct, Chain-of-Thought, Tree-of-Thoughts, and Language Agent Tree Search) to orchestrate decision-making through serial, parallel, or hierarchical state machines depending on task complexity. Tool execution invokes external systems through function calling, with results integrating back into working context. Failures trigger retry logic with exponential backoff or fallback strategies.

Choosing the right infrastructure for agentic systems

Your infrastructure choices determine what your agents can actually do. Building a demo is easy. Building something that handles production load requires careful architecture.

Production systems demand GPU compute (40–80GB VRAM for serving large models), low-latency storage with sub-millisecond access, high-bandwidth networking (100+ Gbps with RDMA capabilities), and event streaming platforms for real-time data pipelines. Converting models from FP16 to INT8 or INT4 precision can reduce VRAM requirements by up to 10x, though this trades off some accuracy.

The challenge is that these requirements typically mean integrating multiple specialized systems: a vector database here, a cache there, separate message queues, and distinct storage layers. Each integration adds latency and operational overhead.

Unified platforms

Unified platforms address this complexity by consolidating capabilities. Redis provides vectors, caching, memory, and queues in one platform with sub-millisecond access times, eliminating the overhead of managing multiple specialized systems.

Redis 8 introduced Vector Sets as a native data type optimized for AI workloads. Powered by the Redis Query Engine, these capabilities support vector search, full-text retrieval, and hybrid search without requiring a separate vector database.

Semantic caching

LLM API calls for similar queries create a critical cost problem. Redis LangCache addresses this through semantic caching that recognizes when different questions mean the same thing. By serving cached responses for semantically similar queries, you can reduce inference costs by 50–80%. At scale, semantic caching becomes a budget requirement, not an optimization.

Consolidating the stack

For LangGraph-based agents, Redis handles the complete stack: short-term memory via checkpointers, long-term memory via Redis Cloud storage, vector database for retrieval, semantic caching through Redis LangCache, and rate limiting for API management. Instead of integrating five separate systems, you consolidate infrastructure.

The architectural advantage is latency reduction. When all your agent's data lives in the same system with sub-millisecond access times, you eliminate the network hops between specialized databases.

Building agents that work

Agentic AI shifts from reactive to autonomous systems. The five components—perception, reasoning, memory, action, and learning—work in continuous loops where each depends on the others.

Redis provides a unified platform with native vector search, semantic caching through Redis LangCache, agent memory storage, and message queuing via Redis Streams. This consolidation reduces operational complexity and improves latency with sub-millisecond access times across all components.

Ready to build production-ready agentic systems? Try Redis free to see how unified infrastructure accelerates development, or talk to our team about architecting your AI infrastructure for scale.