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World Congress 2025 • Aug 20, 2025 • Session details

The AI Agent Path to Prod: Building for Reliability

Max Tkacz

Stop letting probabilistic AI break your production environments. Learn how to isolate deterministic routing and treat evaluations like unit tests to deploy reliable agents at scale.

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#1 about 3 min

Introduction to building reliable AI agents in production

Overcoming the experimental nature of AI tools requires strict evaluation and testing frameworks before enterprise deployment.

#2 about 2 min

Defining realistic scopes for task-based AI agents

Focusing on narrow, specific tasks like trial extensions avoids the compounding failure rates of overly broad agent deployments.

#3 about 3 min

Mapping the path from prototype to production deployment

Moving past initial prototypes involves a structured cycle of scoping, evaluations, guardrails, and continuous monitoring.

#4 about 2 min

Structuring parent workflows and sub-workflow AI agents

Isolating webhook ingestion and data enrichment from core AI logic simplifies both independent testing and workflow maintainability.

#5 about 3 min

Executing and analyzing the core AI agent sub-workflow

Observing an agent process structured parameters reveals how underlying tool dependencies interact with the core language model.

#6 about 5 min

Running happy path evaluations to test agent consistency

Applying repetitive evaluations to standard inputs exposes hidden inconsistencies in tool selection caused by probabilistic model variance.

#7 about 4 min

Iterating on system prompts using evaluation feedback loops

Injecting explicit constraints and few-shot examples directly into the system prompt stabilizes inconsistent output formats and behavior.

#8 about 5 min

Testing edge cases and mitigating prompt injection attacks

Designing custom tests for bad actors prevents prompt injection by routing malicious manipulations to a secure human fallback.

#9 about 5 min

Implementing robust production guardrails and error handling

Building custom error fallback structures and deterministic routing logic proactively manages unpredictable downtime and protects high-value segments.

#10 about 2 min

Ensuring inference redundancy and final deployment takeaways

Configuring fallback models and intelligent routers provides the ultimate layer of stability for automated tasks in production.

Matching moments

2:43 min

Best practices for implementing reliable AI agent frameworks

Marcel Scherenberg Marcel Scherenberg · World Congress 2025

3:34 min

Architecting autonomous agents for production lifecycle management

Sebastian Kister Sebastian Kister · World Congress 2026 Europe

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · World Congress 2025

1:59 min

Shifting from AI experimentation to real-world production

General Program · World Congress 2026 Europe

1:09 min

Resolving developer challenges in AI agent implementation

Ricardo Ricardo · World Congress 2025

2:20 min

Best practices for deploying safe infrastructure AI agents

Alfonso Sandoval Rosas Alfonso Sandoval Rosas · Europe 2026 Virtual

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