LLM Agents: Architecture Patterns for Production Systems

The gap between agent demos and production systems has narrowed significantly. Here’s what works.

Agent Architecture Fundamentals

The Core Loop

Every LLM agent executes variations of this cycle:

┌─────────────────────────────────────────┐
│           Agent Loop                    │
├─────────────────────────────────────────┤
│  1. OBSERVE → Parse environment state   │
│  2. REASON  → Think about next action   │
│  3. ACT     → Execute tool/API call      │
│  4. EVALUATE → Check result quality     │
│  5. LOOP    → Continue or finish         │
└─────────────────────────────────────────┘

Key Components

Component	Purpose	Implementation
Memory	Store state and context	Vector DB, key-value
Tools	Extend capabilities	APIs, code execution
Planning	Decompose tasks	Chain-of-thought, tree search
Reflection	Self-correct	Critique prompts, verification

Architecture Patterns

Pattern 1: Tool-Augmented Agent

User Intent → LLM → Tool Selection → Execution → Response
                    ↓
              Fallback: Human Review

Best for: Single-turn tasks, research, data retrieval

Pattern 2: ReAct Agent

Combines Reasoning + Acting:

Thought: I need to find X
Action: search(query=X)
Observation: Found Y
Thought: Y isn't what I need, let me refine
Action: search(query="X detailed")
...

Best for: Complex multi-step reasoning, debugging

Pattern 3: Plan-and-Execute

Separates planning from execution:

PLANNER: "Break task into steps" → [Step 1, Step 2, Step 3]
EXECUTOR: Execute each step → [Result 1, Result 2, Result 3]
SYNTHESIZER: Combine results → Final output

Best for: Complex tasks where planning matters more than speed

Pattern 4: Hierarchical Agents

Manager Agent
├── Specialist Agent A (research)
├── Specialist Agent B (coding)
├── Specialist Agent C (review)
└── Specialist Agent D (testing)

Best for: Enterprise workflows, parallel task execution

Tool Design Principles

Good Tool Characteristics

Principle	Example
Idempotent	Same input = same output
Atomic	Does one thing well
Observable	Returns clear success/failure
Documented	Clear inputs/outputs/schema

Tool Categories

1. Information Retrieval: Web search, database queries, API calls
2. Code Execution: Python REPL, shell commands, sandboxed environments
3. File Operations: Read, write, append with access controls
4. External Systems: Email, Slack, CRM, calendar

Memory Architectures

Memory Types

Type	Contents	Use Case
Short-term	Current conversation	Context preservation
Working	Session state	Task continuity
Long-term	Historical interactions	Learning from past
Semantic	Embeddings of facts	Knowledge retrieval

Vector DB Integration

Query → Embed → Similarity Search → Retrieve Top-K → Augment Context

Popular options: Pinecone, Weaviate, pgvector, Chroma

Production Considerations

Error Handling

class AgentError(Exception):
    pass
 
def execute_with_retry(agent, max_retries=3):
    for attempt in range(max_retries):
        try:
            return agent.run()
        except AgentError as e:
            if attempt == max_retries - 1:
                raise
            # Fallback: human review or simpler approach
            return escalate_to_human(e)

Cost Management

• Token budgets per conversation
• Early stopping for simple tasks
• Cache repeated operations
• Selective context injection

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Media & Sources

Embedded Images

Source Links

• ReAct Paper
• LangChain Agent Documentation
• AutoGPT Architecture Analysis