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Building AI Agents That Actually Work

5 min read

Everyone's talking about AI agents. Few are shipping them.

The gap isn't capability—modern LLMs are remarkably powerful. The gap is architecture. Most agent projects fail because they're designed as demos, not systems.

I've built agents that run in production every day: marketing analytics agents, SEO audit agents, voice scheduling agents, document processing agents. Here's what I've learned about making them work.

What Makes an Agent Actually Useful

An agent that works in production needs three things:

  1. Clear scope — It solves one problem well, not every problem poorly
  2. Real integrations — It connects to the systems where work actually happens
  3. Graceful failure — When it can't handle something, it escalates cleanly

Let me walk through each.

Clear Scope

The temptation is to build a general-purpose assistant. "It can do anything!" sounds great in a pitch deck. In practice, it means the agent does nothing reliably.

Pick one workflow. Understand it deeply. Build for that.

My best agents are boring. They pull data from three sources, run some analysis, and produce a structured output. No creativity required—just consistent execution.

Here's an example. My marketing performance agent does exactly this:

  1. Pulls data from GA4, Google Ads, and Search Console
  2. Calculates week-over-week and month-over-month changes
  3. Identifies anomalies that exceed statistical thresholds
  4. Generates a structured report with specific recommendations
  5. Posts to Slack every Monday at 8am

That's it. No chat interface. No "ask me anything." Just reliable, useful output that people actually use because it fits into their existing workflow.

Real Integrations

This is where most projects die. The agent needs to:

  • Read from your data sources (APIs, databases, files)
  • Write to your action targets (Slack, email, CRMs, calendars)
  • Handle auth, rate limits, and schema changes

If you can't answer "where does the data come from?" and "where does the output go?" with specific system names, you're not ready to build.

Here's my integration checklist:

## Data Sources
- [ ] API access confirmed and tested
- [ ] Auth method documented (OAuth, API key, etc.)
- [ ] Rate limits understood and respected
- [ ] Schema changes have notification path
- [ ] Fallback behavior defined for outages

## Action Targets
- [ ] Output format matches target system requirements
- [ ] Permissions configured and tested
- [ ] Error handling for failed writes
- [ ] Confirmation/logging of successful actions

## Monitoring
- [ ] Health checks on all integrations
- [ ] Alerting on failures
- [ ] Metrics on latency and success rates

I spend more time on this checklist than on prompt engineering. The integration layer is where agents succeed or fail.

Graceful Failure

Agents will hit edge cases. The question is what happens next.

Good agent design includes:

  • Confidence thresholds — When the model isn't sure, say so. Include confidence scores in outputs. Set thresholds that trigger human review.

  • Fallback paths — When an integration fails, what happens? The agent should degrade gracefully, not crash silently.

  • Clear logging — Every action the agent takes should be logged with reasoning. When something goes wrong, you need to understand why.

The goal isn't perfection—it's predictability. Stakeholders trust systems they can understand.

Here's how I handle uncertainty:

interface AgentOutput {
  recommendation: string;
  confidence: "high" | "medium" | "low";
  reasoning: string[];
  dataQuality: {
    completeness: number;
    freshness: Date;
    sources: string[];
  };
  requiresReview: boolean;
}

// If confidence is low OR data quality is poor, flag for review
const requiresReview =
  output.confidence === "low" ||
  output.dataQuality.completeness < 0.8;

The Integration-First Approach

When I start a new agent project, I build the integration layer first. Before any LLM logic, I have:

  1. A working data pipeline — I can pull all required data and it's in a clean format
  2. A working output path — I can send test messages to the target system
  3. A working monitoring setup — I can see what's happening and get alerted on failures

Only then do I add the "AI" part. And often, that's the easy part. Modern LLMs are good at analysis and summarization. The hard part is everything around them.

Getting Started

If you're building your first agent:

  1. Find the right workflow — Manual, repetitive, well-documented. Bonus if the stakes are low for the first iteration.

  2. Map the data — Where does information come from? What format? What's the update frequency?

  3. Map the actions — Where do outputs go? What format do they need to be in? Who reviews them?

  4. Build the pipeline — Get data flowing end-to-end before adding intelligence.

  5. Deploy to one user — Get feedback from real usage. Iterate. Expand slowly.

The unglamorous truth: agent development is 80% integration work and 20% AI work. Get the 80% right, and the 20% almost writes itself.

Want to discuss your agent project? Get in touch.