AI Agents in Practice: Building Autonomous Systems That Deliver

AI agents have moved from research curiosity to production reality. At TiaTech, we’ve been building and deploying agent-based systems for clients across industries. Here’s what we’ve learned about making them reliable.

What Makes an Agent Different from a Chatbot

A chatbot responds to prompts. An agent takes action. The distinction matters because agents interact with real systems — databases, APIs, file systems, deployment pipelines — and mistakes have consequences.

Chatbot: "Here's how you could update the database..."
Agent:    UPDATE users SET status = 'active' WHERE id = 42; ✓ Done.

The gap between these two is where engineering discipline lives.

The Architecture That Works

After building several agent systems, we’ve converged on a pattern we call Plan → Verify → Execute → Report:

1. Plan    — Agent breaks the task into steps
2. Verify  — Each step is validated before execution
3. Execute — Actions are taken with rollback capability
4. Report  — Results are logged and surfaced to the user

RAG: The Foundation Layer

Retrieval-Augmented Generation gives agents access to your data without fine-tuning. The key insight: chunk size matters more than embedding model choice.

# Effective chunking strategy
chunk_config = {
    "size": 512,          # tokens, not characters
    "overlap": 64,        # context continuity
    "strategy": "semantic" # split on paragraph boundaries
}

We’ve found that 512-token chunks with semantic splitting outperform larger chunks in retrieval accuracy for most business documents.

Tool Calling: Where Agents Get Useful

Modern LLMs can call functions. The trick is designing tools that are safe by default:

@tool(requires_confirmation=True)
def deploy_to_production(service: str, version: str) -> str:
    """Deploy a service version to production.
    Requires human confirmation before executing."""
    validate_version(service, version)
    return orchestrator.deploy(service, version)

Every tool that modifies state should require confirmation. Read-only tools can run freely.

Error Handling in Agent Systems

Agents will fail. The question is how gracefully. We implement three layers of error handling:

Layer 1: Input validation — Catch bad inputs before they reach the LLM.

Layer 2: Output parsing — Validate the LLM’s response matches expected schemas.

Layer 3: Execution guards — Rate limits, permission checks, and circuit breakers on tool calls.

class AgentGuard:
    max_actions_per_minute = 10
    max_cost_per_session = 5.00  # USD
    blocked_operations = ["DROP", "DELETE FROM", "rm -rf"]

Monitoring and Observability

You can’t manage what you can’t measure. Every agent session should track:

• Token usage — Cost per task
• Tool call success rate — Which tools fail most
• Latency breakdown — Where time is spent
• User satisfaction — Did the agent actually help

When Not to Use Agents

Not every problem needs an agent. Simple rule-based automation is faster, cheaper, and more predictable for:

• Scheduled data transformations
• Template-based report generation
• Form validation and routing
• Notification systems

Use agents when tasks require judgment, multi-step reasoning, or dynamic tool selection.

Getting Started

Start small. Pick one well-defined workflow, build an agent for it, measure the results, then expand. The companies succeeding with AI agents aren’t the ones with the most sophisticated models — they’re the ones with the best engineering practices around them.

At TiaTech, we help businesses identify the right use cases and build agent systems that are reliable, observable, and cost-effective. Get in touch to discuss your automation goals.