AI Agents Testing before deploying to production
Summary
Discusses best practices for testing AI agents before deploying them to production environments.
Similar Articles
How are you testing your agents before deploying? Or is everyone just vibes-checking in prod?
A discussion on the challenges of testing non-deterministic AI agents, questioning how developers validate tool usage, behavior, and multi-step workflows without traditional testing patterns.
AI Agents in Production: The Failure Modes Nobody Puts in the Demo
A practical deep-dive on the real-world challenges of deploying AI agents in production, covering the gap between demos and reliable systems, attack surfaces like prompt injection, and design principles for safe autonomy.
10 things I'd tell anyone starting to build AI agents in production
A practitioner shares ten critical lessons for deploying AI agents in production, emphasizing code-based constraints, context management, and security over relying solely on prompts.
@bibryam: Taking AI agents to production takes more than deployment: • governed data and memory • evaluation before release • sec…
The tweet highlights key aspects of deploying AI agents in production, including data governance, evaluation, security, and observability, referencing a Google whitepaper on the lifecycle from development to production.
How do you test AI agents before and after deployment? What has actually held up for you?
A practitioner asks the community how they test tool-calling AI agents before and after deployment, comparing scenario tests, LLM-as-judge, tool-call trajectory checks, and regression suites while raising doubts about coverage, judge reliability, and nondeterminism.