Best Practices for Building Agentic AI Systems: What Actually Works in Production

I've been experimenting with adding AI agents to UserJot, our feedback, roadmap, and changelog platform. Not the simple "one prompt, one response" stuff. Real agent systems where multiple specialized agents communicate, delegate tasks, and somehow don't crash into each other.

The Two-Tier Agent Model That Actually Works

After countless experiments and production deployments, I've found that the most reliable pattern is a two-tier architecture:

Stateless Subagents: The Most Important Rule

The biggest breakthrough was making subagents completely stateless. Each subagent receives:

• The complete context it needs
• A specific task to accomplish
• Clear success criteria

No shared memory, no session state, no "remember what we talked about." This eliminates an entire class of bugs and makes the system predictable.

interface SubagentRequest {
  context: CompleteContext
  task: SpecificTask
  successCriteria: string[]
}

interface SubagentResponse {
  result: TaskResult
  confidence: number
  nextSuggestedActions?: Action[]
}

Task Decomposition: How to Break Things Down

The orchestrator's job is breaking complex requests into atomic tasks. Here's what I've learned works:

Good task boundaries:

• Can be completed with a single API call or analysis
• Have clear success/failure conditions
• Don't depend on other tasks' intermediate state

Bad task boundaries:

• Require maintaining conversation context
• Have ambiguous completion criteria
• Need to "check back later"

Communication Protocols That Don't Suck

Most agent communication patterns I see are overcomplicated. Here's what actually works in production:

The Request-Response Pattern

Keep it simple. Agents communicate through structured requests and responses, not natural language chat.

// Good: Structured communication
{
  type: "ANALYZE_FEEDBACK",
  payload: {
    feedbackId: "123",
    analysisType: "sentiment",
    includeActionItems: true
  }
}

// Bad: Natural language between agents
"Hey, can you look at feedback #123 and tell me if it's positive?"

Error Handling That Preserves Context

When a subagent fails, the orchestrator needs enough information to decide what to do next:

interface AgentError {
  type: "RETRY" | "ESCALATE" | "SKIP" | "ABORT"
  reason: string
  suggestedAlternatives?: Alternative[]
  partialResults?: PartialResult[]
}

Agent Specialization Patterns

The Database Agent

Handles all data operations. Knows your schema, understands relationships, can construct efficient queries.

Responsibilities:

• Query construction and optimization
• Data validation and constraints
• Relationship mapping
• Performance monitoring

The Analysis Agent

Processes and interprets data. Handles ML models, statistical analysis, pattern recognition.

Responsibilities:

• Text analysis and NLP
• Trend identification
• Anomaly detection
• Recommendation generation

The Integration Agent

Manages external APIs and services. Handles authentication, rate limiting, data transformation.

Responsibilities:

• API authentication and management
• Rate limiting and retry logic
• Data format transformation
• External service monitoring

Real Production Lessons

Monitor Agent Performance Separately

Each agent type has different performance characteristics. Database agents should be fast and reliable. Analysis agents might be slower but need high accuracy.

Implement Circuit Breakers

When an agent starts failing, stop sending it requests until it recovers. This prevents cascade failures.

Version Your Agent Interfaces

As your agents evolve, you'll need to update their capabilities. Version the interfaces so you can deploy changes gradually.

Keep Logs Structured

When debugging agent interactions, structured logs are essential:

logger.info('Agent request started', {
  agentType: 'analysis',
  requestId: uuid,
  taskType: 'sentiment-analysis',
  contextSize: context.length,
  timestamp: Date.now()
})

What's Next?

This architecture has served us well at UserJot, but we're continuously evolving. Next on our roadmap:

• Adaptive task routing based on agent performance
• Dynamic agent scaling for high-load scenarios
• Cross-agent learning to improve task decomposition

The key is starting simple and adding complexity only when you have specific problems to solve. Most agent systems fail because they're over-engineered from day one.

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Want to see how we're using these patterns at UserJot? Check out our public roadmap or try our feedback management platform yourself.