Agentic AI # Agentic AI Architecture Patterns: Tool-Use Orchestration and Multi-Agent Coordination 2026-03-166 min read [Lucio Durán]() Engineering Manager & AI Solutions Architect Also available in: [Español]() ## Defining Agentic AI Systems An AI system becomes agentic when it moves beyond single-turn prompt-response interactions to autonomously: - **Plan** multi-step strategies to achieve goals - **Execute** actions using tools, APIs, and external services - **Observe** the results of those actions - **Adapt** its approach based on outcomes The key distinction is **autonomy in decision-making**. A chatbot answers questions. An agent decides what to do next. ## Pattern 1: ReAct (Reasoning + Acting) The ReAct pattern is the workhorse of agentic AI. The agent alternates between reasoning about what to do and taking actions: ``` Think → Act → Observe → Think → Act → Observe → ... → Answer ``` This is the pattern behind most tool-use implementations in Claude, GPT-4, and similar models. The LLM receives a prompt with available tools, reasons about which tool to call, executes it, observes the result, and continues until it has enough information to respond. ### When to use ReAct - Single-agent tasks with clear tool boundaries - Information gathering and synthesis - Code generation with execution and iteration - Customer service automation with system integrations ### Production considerations The main risk with ReAct is **infinite loops**. Always implement: - Maximum iteration limits - Token budget guards - Timeout mechanisms - Graceful degradation when the agent gets stuck ## Pattern 2: Plan-and-Execute Plan-and-Execute separates planning from execution into distinct phases: 1. **Planning phase**: The LLM creates a complete plan before any action 2. **Execution phase**: Each step is executed sequentially or in parallel 3. **Re-planning**: If execution fails, the agent re-plans from the current state This pattern works well when tasks are complex but somewhat predictable. The upfront planning reduces token waste from aimless exploration. ### Architecture sketch ``` User Goal → Planner Agent → [Step 1, Step 2, Step 3, ...] ↓ Executor Agent(s) → Results ↓ Re-planner (if needed) ``` ### When to use Plan-and-Execute - Multi-step workflows with known tool capabilities - Tasks where the cost of wrong actions is high - Scenarios requiring user approval before execution ## Pattern 3: Multi-Agent Orchestration Multi-Agent systems decompose complex tasks across specialized agents that collaborate: - **Orchestrator**: Routes tasks to appropriate specialist agents - **Specialist agents**: Each handles a specific domain (code, research, data analysis) - **Shared memory**: Agents communicate through a common state or message bus ### Real-world example In an e-commerce context, a multi-agent system might include: - **Product Agent**: Searches catalogs, compares specifications - **Pricing Agent**: Analyzes competitor pricing, applies dynamic rules - **Inventory Agent**: Checks stock levels, handles supplier queries - **Customer Agent**: Manages communication and personalization Each agent has its own tool set, system prompt, and context window, but they coordinate through a shared orchestration layer. ### The coordination challenge The hardest part of multi-agent systems isn't building individual agents, it's the coordination layer. Key decisions: - **Message passing**: How agents communicate (shared state vs message queue) - **Conflict resolution**: What happens when agents disagree - **Error propagation**: How failures in one agent affect others - **Context management**: Preventing context window overflow across agents ## Pattern 4: Tool-Use with Function Calling The simplest agentic pattern, and often the most effective. The LLM has access to a defined set of functions and decides when and how to call them. This is the foundation of the Model Context Protocol (MCP), which standardizes how AI models interact with external tools and data sources. ### MCP as Infrastructure MCP defines a universal protocol for tool discovery and invocation. Instead of each agent implementing custom tool integrations, MCP provides: - **Tool discovery**: Agents can discover available tools dynamically - **Standardized invocation**: Consistent request/response format - **Security boundaries**: Controlled access to resources - **Composability**: Tools from different providers work together ## Choosing the Right Pattern | Pattern | Complexity | Best For | Risk | | --- | --- | --- | --- | | ReAct | Low | Single tasks, tool use | Infinite loops | | Plan-and-Execute | Medium | Multi-step workflows | Over-planning | | Multi-Agent | High | Complex domains | Coordination overhead | | Tool-Use (MCP) | Low-Medium | Standardized integrations | Tool reliability | The most common mistake is over-engineering. Start with simple tool-use, graduate to ReAct when you need reasoning loops, and only reach for multi-agent when you have genuinely separate domains of expertise. ## Infrastructure Decisions That Matter Building agentic systems requires infrastructure decisions that are different from traditional web applications: ### State Management Agents need persistent state between turns. Options include: - **In-memory**: Fast but lost on restart. Fine for short-lived agents - **Database-backed**: Use a database for conversation state and tool results - **Event-sourced**: Store every action as an event for full replay capability ### Observability You cannot debug agentic systems without proper observability: - Log every LLM call with full prompt, response, and token usage - Trace tool invocations with inputs, outputs, and latency - Track agent decision trees for post-mortem analysis - Monitor cost per task (LLM tokens are expensive at scale) ### Cost Control Agent tasks can spiral in cost if not carefully managed: - Set per-task token budgets - Cache tool results aggressively - Use smaller models for routing and classification - Reserve large models for complex reasoning steps ## Future Directions The agentic AI landscape is evolving rapidly. The patterns described here will evolve, but the fundamental principles, clear tool boundaries, explicit planning, robust error handling, and cost awareness, will remain relevant regardless of which models or frameworks emerge. The most important principle for production agentic systems: **start simple, measure everything, and let real usage data guide architecture decisions**. ## Reproducible implementation: tool recovery [Agent Replay Runtime]() implements event history, idempotency keys and resumable JSON plans. Its matrix kills processes after recording a request, after committing its effect and after recording acknowledgment. The effect store is independent of the runtime log. All three idempotent-adapter cases finished with one effect. The blind-retry baseline produced two. Without idempotency, an uncertain request pauses until external evidence is available: a retry cannot manufacture the missing acknowledgment. The project page includes traces, the adapter contract and reproduction commands. This experiment covers local processes and sequential plans, not power-loss recovery or distributed coordination. ## Frequently asked questions ### What is agentic AI? Agentic AI refers to AI systems that can autonomously plan, reason, and execute multi-step tasks using tools, APIs, and other resources, going beyond simple prompt-response interactions. ### What are the main architecture patterns for agentic AI? The four main patterns are: ReAct (Reasoning + Acting), Plan-and-Execute, Multi-Agent Orchestration, and Tool-Use with Function Calling. Each suits different complexity levels and use cases. ### When should I use multi-agent systems vs single-agent? Use single-agent for straightforward tool-use tasks. Use multi-agent when tasks require specialized expertise, parallel processing, or when different sub-tasks have fundamentally different context requirements. - agentic-ai - architecture - llm - ai-agents - tool-use - multi-agent [Back to Blog]()