10. AI Agent Architectures

An AI Agent architecture is the blueprint that brings together all the components — planning, reasoning, memory, tools, execution, observation, and reflection — into a cohesive, production-ready system.

This document is the culmination of everything you’ve learned in Chunk 1. It presents the complete reference architectures for building AI Agents, from simple designs suitable for prototypes to enterprise-grade systems capable of handling complex, multi-step tasks reliably.

flowchart TD
    GOAL["🎯 User Goal"] --> PLAN["📋 Planner\nTask Decomposition"]
    PLAN --> LLM["🧠 LLM Brain\nReasoning Engine"]
    LLM --> MEM["💾 Memory\nWorking + Long-Term"]
    LLM --> TOOLS["🛠️ Tool Registry\nAvailable Actions"]
    TOOLS --> EXEC["⚡ Execution Engine\nRuns Actions"]
    EXEC --> OBS["👁️ Observer\nCollects Results"]
    OBS --> REFLECT["🪞 Reflection\nEvaluates Outcome"]
    REFLECT -->|"Continue"| PLAN
    REFLECT -->|"Complete"| RESULT["✅ Task Complete"]
    MEM -.-> PLAN
    MEM -.-> LLM
    MEM -.-> OBS

    style GOAL fill:#3b82f6,color:#fff
    style PLAN fill:#8b5cf6,color:#fff
    style LLM fill:#f59e0b,color:#fff
    style MEM fill:#22c55e,color:#fff
    style TOOLS fill:#ef4444,color:#fff
    style EXEC fill:#6366f1,color:#fff
    style OBS fill:#ec4899,color:#fff
    style REFLECT fill:#f59e0b,color:#fff
    style RESULT fill:#22c55e,color:#fff

You’ve learned about planning, reasoning, memory, tools, the agent loop, design patterns, and multi-agent systems. But knowing the components isn’t enough — you need to know how to assemble them into a working system.

A good architecture:

1. Defines component boundaries — What does each component own?
2. Specifies data flow — How does information move between components?
3. Establishes protocols — How do components communicate?
4. Enables scaling — How does the system grow with load?
5. Provides failure modes — What happens when components fail?

An airport control tower doesn’t have a single person doing everything. It has multiple specialized roles working together through established protocols:

• Flight Planner — Creates the flight plan (Planner)
• Air Traffic Controller — Makes real-time decisions (LLM Brain)
• Log Book — Records all flights and incidents (Memory)
• Radar & Radio — Tools for communication and tracking (Tool Registry)
• Runway Controller — Executes takeoffs and landings (Execution Engine)
• Weather Station — Monitors conditions (Observer)
• Post-Flight Review — Analyzes what went well and what didn’t (Reflection)

Each component has a clear role. Information flows through defined channels. When something fails, there are backup procedures. This is exactly how a good Agent architecture should work.

Best for: Single-step tasks, prototypes, personal use

flowchart LR
    PROMPT["User Prompt"] --> LLM["🧠 LLM\n(GPT-4o / Claude)"]
    LLM --> TOOL["🛠️ Optional Tool Call"]
    TOOL --> LLM
    LLM --> RESPONSE["Response"]

    style PROMPT fill:#3b82f6,color:#fff
    style LLM fill:#8b5cf6,color:#fff
    style TOOL fill:#f59e0b,color:#fff
    style RESPONSE fill:#22c55e,color:#fff

Components: LLM + 1-3 tools Memory: Context window only Planning: None (single response) Iteration: None (single pass) Cost: Very low Example: A chatbot that can search the web or check the weather

Best for: Multi-step tasks, research, content creation

flowchart TD
    GOAL["🎯 Goal"] --> AGENT["🤖 Agent Orchestrator"]

    AGENT --> AGENT_LOOP["🔄 Agent Loop\n(Think → Plan → Act → Observe → Reflect)"]

    AGENT_LOOP --> LLM["🧠 LLM Brain"]
    AGENT_LOOP --> TOOLS["🛠️ Tool Registry\n(5-10 tools)"]
    AGENT_LOOP --> MEM["💾 Memory\n(Working + Short-term)"]

    LLM --> TOOLS
    TOOLS --> LLM
    MEM -.-> LLM

    AGENT_LOOP -->|"Complete"| RESULT["✅ Result"]
    AGENT_LOOP -->|"Max iterations"| FAIL["❌ Timeout / Failure"]

    style GOAL fill:#3b82f6,color:#fff
    style AGENT fill:#8b5cf6,color:#fff
    style AGENT_LOOP fill:#f59e0b,color:#fff
    style LLM fill:#22c55e,color:#fff
    style TOOLS fill:#ef4444,color:#fff
    style MEM fill:#6366f1,color:#fff
    style RESULT fill:#22c55e,color:#fff
    style FAIL fill:#ef4444,color:#fff

Components: Agent orchestrator + LLM + 5-10 tools + working/short-term memory Memory: Working memory (RAM) + short-term (context window) Planning: Dynamic planning per iteration Iteration: Up to 15-25 iterations with reflection Cost: Low to moderate Example: Research assistant that searches, reads, synthesizes, and writes reports

Best for: Enterprise automation, customer support, data processing

flowchart TD
    subgraph INGRESS["Ingress Layer"]
        API["🌐 API Gateway"]
        AUTH["🔐 Authentication"]
        RATE["⏱️ Rate Limiter"]
    end

    subgraph ORCH["Orchestration Layer"]
        ROUTER["🔀 Router"]
        SUP["👤 Supervisor Agent"]
        PLAN["📋 Planner"]
        MONITOR["📊 Monitor"]
    end

    subgraph AGENTS["Agent Pool"]
        AG1["🤖 Agent 1\n(Research)"]
        AG2["🤖 Agent 2\n(Coding)"]
        AG3["🤖 Agent 3\n(Analysis)"]
        AG4["🤖 Agent 4\n(Review)"]
    end

    subgraph INFRA["Infrastructure"]
        LLM_SVC["🧠 LLM Service\n(API or self-hosted)"]
        VDB[("🗄️ Vector DB\n(Memory Store)")]
        REDIS[("⚡ Cache\n(Redis)")]
        QUEUE[("📨 Message Queue")]
        LOGS[("📝 Audit Log")]
    end

    USER["User"] --> INGRESS --> ORCH --> AGENTS
    AGENTS --> INFRA
    MONITOR -.-> AGENTS
    LOGS -.-> ALL

    style INGRESS fill:#3b82f6,color:#fff
    style ORCH fill:#8b5cf6,color:#fff
    style AGENTS fill:#f59e0b,color:#fff
    style INFRA fill:#22c55e,color:#fff

Components: Full stack with API gateway, auth, routing, supervisor, agent pool, message queue, caching, monitoring, audit logging Memory: Working (RAM) + short-term (context) + long-term (vector DB) Planning: Multi-level (strategic → tactical → operational) Iteration: Up to 50 iterations with sophisticated reflection and error recovery Cost: Moderate to high Example: Enterprise customer support system handling thousands of tickets per day

Best for: Complex enterprise workflows, software development, research platforms

sequenceDiagram
    participant User
    participant Gateway as API Gateway
    participant Router
    participant Orch as Orchestrator
    participant Planner
    participant AgentA as Research Agent
    participant AgentB as Code Agent
    participant AgentC as Review Agent
    participant VectorDB as Vector DB
    participant LLM

    User->>Gateway: "Build a user management system"
    Gateway->>Router: Authenticate & route
    Router->>Orch: Route to orchestrator

    Orch->>Planner: Create execution plan
    Planner->>Orch: Plan: Research → Design → Code → Review → Deploy

    Orch->>AgentA: Research best patterns
    AgentA->>VectorDB: Query relevant examples
    AgentA->>LLM: Analyze requirements
    AgentA-->>Orch: Recommended: RBAC + JWT + PostgreSQL

    Orch->>Planner: Update plan with recommendations
    Planner-->>Orch: Updated plan with specific technologies

    Orch->>AgentB: Implement user service
    AgentB->>LLM: Generate code
    AgentB-->>Orch: Code generated

    Orch->>AgentC: Review code quality
    AgentC->>LLM: Analyze for issues
    AgentC-->>Orch: Issues found: 2 security concerns

    Orch->>AgentB: Fix security issues
    AgentB-->>Orch: Issues resolved

    Orch->>AgentC: Re-review
    AgentC-->>Orch: ✅ Approved

    Orch-->>User: "User management system complete. 3 files created, all tests passing."

Components: Full multi-agent with orchestrator, planner, specialized agents, review agents, vector DB, caching, monitoring, audit Memory: Full memory stack (working + short-term + long-term + episodic + semantic) Planning: Hierarchical dynamic planning with re-planning and rollback Iteration: Up to 500 iterations Cost: High Example: Devin, Manus, advanced enterprise automation platforms

flowchart LR
    subgraph LEVELS["Architecture Levels"]
        L1["Level 1: Simple\nCost: $ | Complexity: 1/5"]
        L2["Level 2: Standard\nCost: $$ | Complexity: 2/5"]
        L3["Level 3: Production\nCost: $$$ | Complexity: 4/5"]
        L4["Level 4: Enterprise\nCost: $$$$ | Complexity: 5/5"]
    end

    L1 --> L2 --> L3 --> L4

    style L1 fill:#3b82f6,color:#fff
    style L2 fill:#8b5cf6,color:#fff
    style L3 fill:#f59e0b,color:#fff
    style L4 fill:#ef4444,color:#fff

flowchart TD
    Q1["How many users?"]
    Q1 -->|"1-10"| Q2["Task complexity?"]
    Q1 -->|"10-1000"| Q3["Need quality review?"]
    Q1 -->|"1000+"| L3["Architecture Level 3+"]

    Q2 -->|"Simple (1-2 steps)"| L1["Level 1: Simple Agent"]
    Q2 -->|"Complex (5+ steps)"| L2["Level 2: Standard Agent"]

    Q3 -->|"Yes"| L3["Level 3: Production Agent"]
    Q3 -->|"No"| L2

    L1 --> COST1["Cost: < $100/mo"]
    L2 --> COST2["Cost: $100-500/mo"]
    L3 --> COST3["Cost: $500-3000/mo"]
    Q1 -->|"Enterprise"| L4["Level 4: Enterprise"]
    L4 --> COST4["Cost: $3000+/mo"]

    style L1 fill:#22c55e,color:#fff
    style L2 fill:#3b82f6,color:#fff
    style L3 fill:#f59e0b,color:#fff
    style L4 fill:#ef4444,color:#fff

flowchart TD
    subgraph PROD["Production System Requirements"]
        RELIABILITY["🔒 Reliability\n- Retry logic\n- Circuit breakers\n- Graceful degradation"]
        SCALABILITY["📈 Scalability\n- Horizontal scaling\n- Queue-based processing\n- Stateless agents"]
        OBSERVABILITY["👁️ Observability\n- Metrics (latency, cost, errors)\n- Traces (per-request)\n- Logs (per-action)"]
        SAFETY["🛡️ Safety\n- Human-in-the-loop\n- Approval gates\n- PII filtering\n- Rate limiting"]
        COST["💰 Cost Management\n- Token tracking\n- Tool call budgets\n- Model tier selection\n- Caching"]
    end

    PROD --> |"Implemented in\nLevel 3+"| SYSTEM["Production-Ready\nAgent System"]

    style RELIABILITY fill:#3b82f6,color:#fff
    style SCALABILITY fill:#8b5cf6,color:#fff
    style OBSERVABILITY fill:#f59e0b,color:#fff
    style SAFETY fill:#22c55e,color:#fff
    style COST fill:#ef4444,color:#fff
    style SYSTEM fill:#6366f1,color:#fff

1. Start at Level 1, evolve up — Build a simple working agent first, then add complexity only as needed
2. Design for failure — Every component should have a fallback. LLM API down → use cached response. Tool fails → retry with different parameters
3. Log everything — Every thought, action, observation, and reflection should be logged with timestamps
4. Set budgets — Token limit per task, cost limit per user, iteration limit per run
5. Test with real tasks — Use the tasks your users will actually perform, not synthetic benchmarks
6. Monitor in production — Track latency per component, cost per task, completion rate, error rate
7. Iterate on prompts — The LLM prompts for planning, reasoning, and reflection need continuous refinement

Q: What are the four levels of Agent architecture?

Level 1: Simple Agent (LLM + few tools). Level 2: Standard Agent (agent loop + planning + reflection). Level 3: Production Agent (multi-agent + memory + caching + monitoring). Level 4: Enterprise Multi-Agent (full system with hierarchical planning, audit, human-in-the-loop).

Q: What’s the most important component of an agent architecture?

The Agent Loop. Without the loop, you just have an LLM with tools — no iteration, no reflection, no improvement. The loop is what makes an agent an agent.

Q: When should you add long-term memory (vector DB) to your agent architecture?

When agents need to remember information across sessions or learn from past experiences. For single-session tasks, working memory (RAM) + short-term memory (context window) is sufficient. Add vector DB when: (1) users run tasks daily and expect the agent to remember preferences, (2) the agent should learn from past mistakes, (3) you need to store knowledge gained from previous tasks.

Q: Design an architecture that supports both synchronous (real-time chat) and asynchronous (long-running research) agent tasks.

Synchronous path: Direct agent execution with streaming. User sends message → agent processes → streams response in real-time. Timeout: 30 seconds. Asynchronous path: Queue-based execution. User submits task → task goes to queue → agent processes in background → user receives notification when done. Router: Classifies tasks as sync or async based on expected duration. Tasks expected to take < 30s → sync. Tasks > 30s → async. State store: Both paths persist intermediate state for recovery. Benefits: Users get fast responses for simple tasks while complex tasks run in background without blocking.

Q: How do you handle an LLM API outage in a production agent system?

Immediate failover: Route to a secondary LLM provider (OpenAI → Anthropic → Gemini). Graceful degradation: If all LLM APIs are down, switch to a cached-response mode: serve answers from the vector DB of past successful completions. Queuing: Queue incoming requests for processing when APIs recover. User communication: Inform users of degraded service and expected recovery time. Testing: Run chaos engineering tests monthly — simulate LLM API failure and verify the system handles it correctly. The goal is never show an error to the user, even during an outage.

Q: Design an agent architecture for a healthcare application that requires HIPAA compliance, audit trails, and zero data leakage between patients.

Per-patient isolation: Each patient gets a separate agent session with dedicated memory store. No data sharing between sessions. Audit trail: Every action logged with patient ID, agent ID, timestamp, action type, full input/output. Logs are append-only and immutable. LLM hosting: Self-hosted LLM or HIPAA-compliant API (Azure OpenAI, AWS Bedrock) — no data sent to non-compliant providers. Access control: Agents can only access the current patient’s records via metadata-filtered vector search. Human review: All generated medical advice requires a healthcare professional’s approval before delivery to patient. Data retention: Automatic deletion of patient data after 30 days unless retention is explicitly required.

Previous: 09 — Agent Design Patterns

Next: Chunk 2 — Building Production AI Agents (Coming Soon)

🎉 Congratulations on completing Phase 6, Chunk 1: AI Agent Fundamentals & Architectures!