Building Production-Ready AI Agents: Lessons from the Trenches

After spending the last two years building AI agents for enterprise systems—from GitLab merge request reviewers to ServiceNow integration tools—I’ve learned that production-ready AI agents require far more than just prompting an LLM. Here’s what actually matters.

The Reality of Production AI Agents

Most discussions about AI agents focus on the exciting parts: reasoning capabilities, tool use, and autonomy. But production deployment reveals a different set of challenges. Your agent needs to handle:

• Reliability under edge cases - What happens when the API times out mid-conversation?
• Observability - How do you debug a decision made by an LLM three steps ago?
• Cost management - Token costs add up quickly at scale
• Error recovery - Graceful degradation when tools fail

Architecture Patterns That Work

Here’s a simplified architecture I use for most production agents:

graph TD
  A[User Input] --> B[Input Validation]
  B --> C[Context Builder]
  C --> D[LLM Orchestrator]
  D --> E{Tool Required?}
  E -->|Yes| F[Tool Executor]
  F --> G[Result Validator]
  G --> D
  E -->|No| H[Response Generator]
  H --> I[Output Formatter]
  I --> J[User]
  
  K[Observability Layer] -.-> B
  K -.-> C
  K -.-> D
  K -.-> F
  K -.-> H

Key components explained:

1. Input Validation

Never trust user input directly. Validate, sanitize, and structure it before sending to the LLM:

from pydantic import BaseModel, Field

class UserQuery(BaseModel):
    query: str = Field(..., min_length=1, max_length=2000)
    context: dict = Field(default_factory=dict)
    
    def sanitize(self):
        # Remove potentially harmful patterns
        self.query = self.query.strip()
        # Add your sanitization logic
        return self

2. Context Builder

Build relevant context intelligently. Don’t dump everything into the prompt:

async def build_context(query: UserQuery, vector_db: VectorStore):
    # Retrieve relevant documents
    docs = await vector_db.similarity_search(
        query.query,
        k=5,
        threshold=0.7
    )
    
    # Prioritize by relevance and recency
    ranked_docs = rank_by_relevance_and_time(docs)
    
    # Fit within token budget
    context = fit_token_budget(ranked_docs, max_tokens=1500)
    
    return context

3. Tool Execution with Retry Logic

Tools will fail. Build resilience from day one:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
async def execute_tool(tool_name: str, params: dict):
    try:
        result = await tools[tool_name].execute(**params)
        return {"success": True, "data": result}
    except Exception as e:
        logger.error(f"Tool {tool_name} failed: {str(e)}")
        return {"success": False, "error": str(e)}

Observability is Non-Negotiable

You cannot debug what you cannot see. Instrument everything:

import structlog
from opentelemetry import trace

logger = structlog.get_logger()
tracer = trace.get_tracer(__name__)

async def agent_loop(query: str):
    with tracer.start_as_current_span("agent_execution") as span:
        span.set_attribute("query_length", len(query))
        
        logger.info("agent.started", query=query[:100])
        
        # Track token usage
        token_counter = TokenCounter()
        
        try:
            response = await llm.generate(
                prompt=build_prompt(query),
                callbacks=[token_counter]
            )
            
            span.set_attribute("tokens_used", token_counter.total)
            logger.info("agent.completed", 
                       tokens=token_counter.total,
                       cost=calculate_cost(token_counter.total))
            
            return response
        except Exception as e:
            span.record_exception(e)
            logger.error("agent.failed", error=str(e))
            raise

Cost Management Strategies

Token costs matter at scale. Here’s what works:

1. Aggressive caching - Cache LLM responses for common queries
2. Smart model selection - Use cheaper models for simple tasks
3. Streaming responses - Start showing results before completion
4. Prompt optimization - Every token counts; compress ruthlessly

Example cost-aware routing:

async def route_to_model(query_complexity: float, budget: float):
    if query_complexity < 0.3 and budget < 0.01:
        return "claude-haiku-4-5"  # Fast and cheap
    elif query_complexity < 0.7:
        return "claude-sonnet-4-5"  # Balanced
    else:
        return "claude-opus-4-1"  # Complex reasoning

Real-World Example: GitLab MR Reviewer

Here’s a simplified version of the GitLab merge request reviewer I built:

class GitLabMRReviewer:
    def __init__(self, llm_client, gitlab_client):
        self.llm = llm_client
        self.gitlab = gitlab_client
        
    async def review_mr(self, project_id: int, mr_id: int):
        # Fetch MR details
        mr = await self.gitlab.get_merge_request(project_id, mr_id)
        diff = await self.gitlab.get_diff(project_id, mr_id)
        
        # Build context
        context = {
            "title": mr.title,
            "description": mr.description,
            "changes": self.parse_diff(diff),
            "project_context": await self.get_project_context(project_id)
        }
        
        # Generate review
        review = await self.llm.generate(
            prompt=self.build_review_prompt(context),
            max_tokens=1500
        )
        
        # Post as comment
        await self.gitlab.create_comment(
            project_id, mr_id, 
            self.format_review(review)
        )
        
        return review

The full implementation includes error handling, rate limiting, and extensive logging—about 500 lines total.

What’s Next

The AI agent space is evolving rapidly. I’m particularly excited about:

• Agent-to-agent communication (A2A protocol) - Enabling complex multi-agent workflows
• Improved tool ecosystems - MCP (Model Context Protocol) standardization
• Better reasoning models - GPT-4o, Claude Opus 4, and beyond

Key Takeaways

Building production AI agents requires:

1. Robust architecture - Plan for failures, not just success paths
2. Comprehensive observability - You can’t improve what you can’t measure
3. Cost awareness - Token costs scale with usage; optimize early
4. Iterative refinement - Your first prompt won’t be your last

The gap between a demo and production is larger than most anticipate. But with the right architectural patterns and operational discipline, AI agents can deliver tremendous value in enterprise environments.

What challenges have you faced building AI agents? I’d love to hear about your experiences. Connect with me on LinkedIn or reach out directly.