Agent-Based Architecture Design

Agent-based architectures have become the dominant pattern for building complex AI systems that require multi-step reasoning, tool use, and autonomous decision-making. This deep dive explores the principles, patterns, and practical considerations for designing production-ready agent systems.

Why Agents?

Traditional ML pipelines are deterministic and stateless: input flows through a series of transformations to produce output. Agents introduce statefulness and decision-making capability. They can:

• Maintain context across multiple interactions
• Decide which tools or actions to take based on current state
• Iterate and refine their approach
• Handle errors and recovery

Core Components

1. Agent Core

The agent core is the reasoning engine—typically an LLM—that processes instructions, maintains state, and decides actions. The agent receives:

• System prompt (defines agent's role and capabilities)
• Current state (conversation history, context)
• Available tools (functions the agent can call)
• User instructions or goals

2. Tool Interface

Tools are deterministic functions the agent can invoke. Each tool has:

• Name and description (used by LLM to decide when to call it)
• Parameters (structured input schema)
• Implementation (actual code that executes)
• Response format (structured output)

Example tools: database queries, API calls, file operations, calculations, external service integrations.

3. State Management

Agent state includes conversation history, intermediate results, and any persistent context. Design considerations:

• Memory limits: LLMs have context windows. Decide how much history to retain.
• State persistence: Store state in database for resumable sessions.
• State compression: Summarize old messages to preserve important context while staying within token limits.

Architecture Patterns

Single Agent with Tools

The simplest pattern: one agent with access to multiple tools. The agent decides which tool to use and how. Good for:

• Workflows with clear decision points
• Systems where a single reasoning loop is sufficient
• Cost-sensitive applications (one LLM call per decision cycle)

Multi-Agent Systems

Multiple specialized agents, each with distinct roles. Agents communicate via shared state or message passing. Good for:

• Complex workflows requiring specialized expertise
• Systems that benefit from separation of concerns
• Parallel processing of independent tasks

Example: Document processing system with parser agent, extraction agent, validation agent, and enrichment agent.

Hierarchical Agents

Orchestrator agent delegates to specialist agents. Orchestrator handles high-level planning; specialists execute specific tasks. Good for:

• Multi-stage workflows with clear phases
• Systems requiring both planning and execution
• Cost optimization (orchestrator uses cheaper model, specialists use expensive ones only when needed)

Implementation Considerations

Tool Design

Well-designed tools are crucial. Principles:

• Atomicity: Each tool should do one thing well
• Idempotency: Calling the same tool with same inputs should produce same outputs
• Error handling: Tools should return structured errors, not throw exceptions
• Descriptive names: LLMs use tool names and descriptions to decide usage

Prompt Engineering

Agent prompts must clearly define:

• Agent's role and objectives
• Available tools and when to use them
• Output format (structured responses for tool calls)
• Error recovery procedures

Use few-shot examples showing correct tool usage patterns.

Loop Control

Agents can get stuck in loops or make unnecessary tool calls. Mitigations:

• Maximum iteration limits
• Cost/time budgets
• Explicit "done" signals
• Human-in-the-loop checkpoints

Production Concerns

Reliability

Agents introduce non-determinism. Ensure:

• Idempotent operations (retries are safe)
• State checkpoints (resume from failures)
• Validation layers (verify agent outputs before critical actions)

Cost Management

Each agent iteration costs tokens. Optimize by:

• Minimizing context size (compress history, remove irrelevant information)
• Using cheaper models for simple decisions
• Caching common tool results
• Setting iteration limits

Observability

Agent systems need extensive logging:

• All agent decisions and reasoning
• Tool calls and results
• State transitions
• Errors and recovery attempts

This enables debugging, optimization, and auditing.

Example: Document Processing Agent

Consider a system that extracts structured data from invoices:

1. Parser Agent: Identifies document type, extracts raw text
2. Extraction Agent: Uses LLM to identify key fields (invoice number, date, amount, vendor)
3. Validation Agent: Checks extracted data against business rules (amounts match, dates are valid)
4. Enrichment Agent: Adds metadata (vendor lookup, category classification)

Each agent is specialized, communicating via structured messages. The coordinator manages workflow state and handles errors (e.g., if extraction fails, retry with different prompt).

Conclusion

Agent-based architectures enable complex, stateful AI workflows that traditional pipelines cannot support. However, they introduce complexity in state management, error handling, and cost control. Successful production systems balance agent autonomy with deterministic validation and clear boundaries.