AI agent architecture patterns: How to choose the right one for your workload

Your architecture choice determines your AI agent's cost structure, reliability, and scaling path before you write a single line of code. Single-agent patterns keep things simple with fewer LLM calls per task, but multi-agent systems can boost performance 81% on parallel tasks, or tank it by up to 70% on sequential tasks if you pick wrong.

A simple chatbot works fine with ReAct and multiple LLM calls per task, but a financial risk assessment system needs parallel multi-agent architecture to analyze transaction patterns, credit risk, and market conditions simultaneously. This guide shows you how to match architecture patterns to your actual workload requirements so you don't waste time rebuilding.

Why your choice of agent architecture determines your AI success

Architecture determines three things that compound against you in production: cost, reliability, and scaling flexibility. Get any of these wrong and you'll spend months rebuilding instead of shipping.

Cost scales with architectural complexity. A ReAct agent handling customer support queries might make 5-7 LLM calls per interaction as it reasons and acts in loops. A planning pattern for that same task often cuts this to 3-4 calls total—1 for planning, then execution—because it creates the complete plan upfront. But planning fails when queries need dynamic adaptation that wasn't in the original plan. Pick the wrong pattern and you're either overpaying for adaptability you don't need, or getting brittle responses that miss edge cases. The difference compounds across thousands of requests.

Those cost problems feed directly into reliability issues. Agent performance drops from 60% to 25% between single execution and eight consecutive runs, a 58% degradation. This isn't something you fix only by swapping models. Architecture and eval discipline heavily affect how inconsistent behavior shows up in production: single-agent systems fail differently than multi-agent orchestrations, and the pattern you choose determines which failure modes you'll spend your time debugging.

The cost and reliability tradeoffs ultimately shape your scaling path. Single-agent systems work fine initially but often need significant redesigns when you add capabilities. Multi-agent systems often add coordination overhead (more calls, more messages, more state), especially as you add specialists, but they scale horizontally. You add specialized agents instead of rewriting your core system. The architecture you pick now determines whether adding features means days of integration or months of rebuilding.

Understanding single-agent patterns

Single-agent architectures work well for focused tasks within one domain. Organizations often start here before adding multi-agent complexity.

ReAct: Reasoning & acting in loops

ReAct alternates between thinking and doing. Your agent reasons about its current state, takes an action, observes the result, then reasons again based on what happened. This loop continues until the task completes.

ReAct works well for tool-heavy workflows with well-defined task domains, where dynamic adaptation between reasoning and action improves interpretability through explicit reasoning traces. Your customer service agent might search your knowledge base, realize it needs more context, query your CRM, then synthesize a response based on both sources. The pattern requires multiple LLM calls per task because each reasoning-action-observation cycle involves at least one model invocation. ReAct tends to struggle when goals span multiple domains and requests get complicated.

Context management becomes critical with ReAct because your agents can hit token limits quickly. Tool schemas and instructions can get huge, sometimes tens of thousands of tokens, so on-demand tool retrieval matters. Let agents write code that orchestrates multiple tools while managing context carefully.

Planning-based agent patterns

Planning agents split strategy from execution. The planner creates a complete plan upfront, then an executor runs each step. Single-query planning is faster but brittle; iterative replanning adapts but costs more. This approach works well for structured tasks, requiring about 1 planning call plus execution calls, which is often more efficient than ReAct's iterative loop.

Scaling beyond one agent: Multi-agent design patterns

Organizations often add multi-agent complexity only when specialization, security boundaries, or multi-domain expertise will meaningfully improve outcomes.

Orchestrator-worker pattern

An orchestrator agent receives tasks and routes them to specialized worker agents. In layered implementations, first-layer workers process their portions and return results to the orchestrator, which synthesizes outputs and dispatches refined tasks to subsequent worker layers. Mixture of Agents (MoA) architectures exemplify this layered approach. You can layer this process multiple times for progressive task improvement.

Use this for parallel analysis of independent factors with real-time coordination. Financial services teams often use it for risk assessment where parallel agents analyze transaction patterns, credit risk, and market conditions simultaneously.

Hierarchical teams with supervisor routing

A supervisor agent manages multiple specialized agents through tool-based handoffs. The supervisor captures queries, routes to appropriate specialists, and orchestrates workflow progression. LangGraph implements this using state graphs, where nodes represent agent actions and edges define routing logic. This pattern works well when you need dynamic task routing and supervisor judgment adds measurable value.

Collaborative sequential & parallel workflows

Sequential patterns chain agents where each builds on the previous output. Parallel patterns let agents handle independent tasks simultaneously, merging results at the end. CrewAI provides a memory system with four core components (short-term, long-term, entity, and external memory) that combine into contextual memory for maintaining coherent agent interactions without explicit message passing.

Reflection, human-in-the-loop & hybrid architectures

Agents in regulated industries need two quality control layers: self-correction before submission and human approval for critical decisions.

Reflection patterns for quality improvement

Reflection lets agents critique their own outputs and refine iteratively. Self-Refine improves outputs via iterative self-feedback, using the same LLM as generator, refiner, and feedback provider.

The Reflexion pattern extends ReAct through five phases: reasoning about the current state, acting on that reasoning, observing results, reflecting on what worked or failed, and repeating the cycle with learned improvements. External evaluation signals inform the reflection step, enabling iterative performance gains. This approach lets language agents significantly improve their problem-solving performance through iterative refinement, though it costs more, typically 2-3x more tokens versus single-pass approaches because of the additional reflection cycles.

When Anthropic's team optimized their agent for SWE-bench in 2024, they spent more time on tool definitions than prompts. That principle continues to hold as agent development matures: tool specification matters more than prompt engineering for production agents.

Human-in-the-loop integration

Human-in-the-loop (HITL) architectures add human oversight at important decision points. LangGraph provides built-in interrupts, approval gates, review checkpoints, and feedback loops. You typically need this for regulated industries, though it adds latency.

The Model Context Protocol (MCP) standardizes how AI apps connect to external context and tools via MCP servers, though the protocol is still evolving with ongoing work on async operations and production scaling. Frameworks like CrewAI and AutoGen define explicit integration patterns alongside MCP. These frameworks handle environment-specific restrictions that distinguish sandbox from production deployments. They also manage approval policies defining which actions need human sign-off, and access controls specifying which users or agents can reach which tools. These are essential capabilities for regulated industries and compliance-heavy workflows.

Teams with solid evaluation infrastructure can upgrade models in days, while those without evaluation systems often face weeks of manual testing. This evaluation capability directly impacts deployment velocity.

Hybrid approaches combining patterns

Production systems typically combine planning, tool use, multi-agent coordination, and reflection. Financial services institutions deploy parallel multi-agent patterns where specialized agents simultaneously process independent risk factors—one analyzing transaction patterns while another evaluates credit risk and a third assesses market conditions—with unified risk decisions synthesized across agent outputs.

Modern production systems integrate human-in-the-loop (HITL) architectures with strategic oversight at important decision points, including active learning for ambiguous cases, interactive machine learning with iterative refinement, and environment-specific restrictions distinguishing sandbox from production access. MCP provides unified interfaces for these hybrid systems as it matures, supporting centralized policy management for access control.

This needs careful state management, clear responsibility boundaries, and explicit control flow to prevent corruption.

How different industries use agent architecture patterns

Different industries optimize for different constraints. Regulatory requirements, latency tolerance, and task structure all influence which patterns work best.

• Financial services uses multiple multi-agent coordination approaches for regulatory compliance, including workflow patterns (with sequential and parallel execution), swarm patterns for distributed reasoning, and graph patterns (hierarchical structures), supplemented by iterative loop patterns for refinement. Insurance underwriting uses parallel patterns where agents simultaneously analyze property, liability, and financial stability, processing risks concurrently with auditable trails.
• Healthcare is deploying hierarchical agent systems for ambient documentation, prior authorization, and patient monitoring. Early implementations demonstrate planning, action, reflection, and memory capabilities with continuous monitoring for patient vitals, progressive risk assessment, and intervention triggers, though human-in-the-loop oversight remains a requirement for regulated workflows.
• E-commerce differentiates by task structure. Customer shopping assistants use ReAct for real-time personalized experiences, accepting higher token costs for dynamic adaptation. Inventory systems use Plan-and-Execute for supply chain coordination, benefiting from predictable sequences and lower costs.

The common thread: match your pattern to your most binding constraint, whether that's compliance, latency, or cost.

Build the right architecture & the right infrastructure

Production agents need three types of state: execution checkpoints for resuming after failures, vector storage for semantic search across past interactions, and in-memory coordination for real-time messaging between agents. Most teams end up stitching together separate tools, each bringing different APIs, failure modes, and bills.

Redis consolidates this into a single platform: vector search for agent memory, semantic caching through Redis LangCache, pub/sub for coordination, and in-memory data structures for operational state. This matters most when you're building multi-agent systems where coordination overhead and latency compound. Redis is the #1 tool for AI agent data storage (43% developer adoption) because it handles all four infrastructure layers with sub-millisecond performance and a unified API.

For simpler single-agent systems, specialized tools often work fine. The consolidation value shows up when you're managing multiple agents, complex state, and tight latency requirements.

Start building production-ready agent systems

Architecture selection determines whether your AI agents work in production or burn budget in endless iteration. Single-agent patterns optimize for simplicity with fewer LLM calls per task. Multi-agent architectures handle complex task decomposition with specialization across multiple domains, but coordination overhead grows as you add agents—more calls, more messages, more state to manage.

Redis Cloud provides the unified infrastructure these architectural decisions require: sub-millisecond latency for coordination, native vector search for agent memory, semantic caching to reduce LLM costs, and pub/sub for event-driven workflows, consolidated into a single platform instead of stitching together separate tools. If your app teams already use Redis for caching, you can extend it to AI workloads instead of adding new vendors, consolidating agent infrastructure into a single real-time data platform.

Try Redis free to test how it handles your agent workload, or talk to our team about production agent infrastructure that scales with your architecture.