Your development team is likely overwhelmed with workflow automation requests. Data processing, content generation, API integrations—these requirements consume time that could be spent on core features. Meanwhile, AI promises to automate these workflows, but most implementations fail in production.

At Wolk, we recently built an AI agent using PydanticAI that writes outbound messages based on web research into companies. Our sales team uses these as starting points for connection requests, saving hours of manual company research while keeping humans in the loop. No automated spam, just smart assistance. The technical challenges taught us what separates successful production deployments from impressive demos that never scale.

This guide shares the architectural decisions that made our agent work reliably in production, based on real experience building systems that handle actual business workflows.

Why most AI Agents never reach production

Building AI agents sounds straightforward until you hit real-world constraints. Your development involves complex technical challenges that don't appear in tutorials:

Infrastructure complexity kills momentum before agents reach users. Local MCP servers, environment-specific dependencies, and complex API orchestration create deployment nightmares that teams can't maintain long-term.

Execution patterns that work in development break under production conditions. Manual triggers work fine for testing, but real workflows need both immediate processing and scheduled automation with proper error handling.

Observability gaps make debugging impossible when things go wrong. Without visibility into LLM API calls, agent decision trees, and data pipeline failures, teams spend weeks troubleshooting issues that should take hours.

Data integration bottlenecks emerge when connecting to existing systems. Custom ETL processes, API rate limiting, and data validation requirements weren't planned for in the initial architecture.

A single production failure can derail entire automation initiatives, making architectural decisions critical from the start.

How we built an Agent that actually works

We used PydanticAI as our framework, deployed on Vercel, integrated Tavily for external data, and added Logfire for monitoring. Each choice solved specific production problems:

Infrastructure & deployment

Stateless cloud deployment eliminates infrastructure headaches. Vercel hosting forced us to design for production from day one—no local dependencies, proper API endpoints, and automatic scaling. The system handles restarts and traffic spikes without breaking.

Structured development with PydanticAI. This framework gives us data validation, type safety, and component isolation. We can test each part independently and catch bugs before deployment. No more "black box" agents that break mysteriously.

Reliable external data with Tavily. Instead of managing our own search infrastructure, we use Tavily's API. It scales automatically and performs consistently across environments without operational overhead.

Execution & processing

Two execution modes for different needs. We built REST endpoints for immediate processing when teams need instant results. Plus webhook automation for scheduled workflows that run without human intervention.

Three-stage data pipeline. Our system processes data through ContactExtraction, CompanyResearch, and MessageDrafting stages. Each stage validates data and handles errors properly.

Controlled outputs with templates. We use structured and approved message templates that our sales team can review and customize before sending. This ensures consistent, professional communication while keeping flexibility and human oversight.

Monitoring & debugging

Complete visibility with Logfire. We track every LLM API call with token usage and latency, log complete agent decision trees with intermediate states, and monitor performance bottlenecks. When something breaks, we know exactly what happened and where.

Automated quality assurance with CI/CD. Every code change triggers a CI/CD pipeline that runs our agent against a test dataset and scores the output quality. This automatically checks if the system still functions correctly and helps us incrementally improving systematically.

What this approach delivers

Our agent researches companies and drafts personalized outbound messages that our sales team reviews and customizes before sending. It processes database records reliably with full visibility into every decision and API call, handling both immediate requests and scheduled automation without manual intervention. This saves hours of manual research per prospect while maintaining human oversight, respecting both our company and our prospects.

More importantly, the system scales. We can add new data sources, modify processing logic, and extend functionality without rebuilding core infrastructure. The monitoring shows us exactly how the system behaves, so we can optimize and troubleshoot confidently.

Key takeaways for your next AI Agent

Start with production in mind.

Choose frameworks like PydanticAI that provide structure and validation. Deploy on platforms like Vercel that force stateless design.

Build for real workflow patterns.

Create both instant processing endpoints and automated scheduling for systematic workflows.

Prioritize observability and validation.

Use tools like Logfire for complete visibility and Pydantic models for data validation at every stage with proper error handling. Implement automated testing with CI/CD pipelines that score output quality on every change.

Keep humans in the loop.

Build assistance tools, not replacement tools. Our agent saves time on research and drafting, but humans review and customize outputs before they go out. This maintains quality while capturing efficiency gains.

At the end of the day, you don't need to build the fanciest AI agent out there. You just need one that's reliable and does its job when your team counts on it!