Building the first AutoML platform for AI agents—enabling systems that automatically evolve, optimize, and redesign themselves to achieve better performance over time.
Continuous Self-Improvement Loop
The Problem
Organizations deploying AI agents face mounting complexity—and no systematic way to escape it.
Engineers must hand-craft prompts, tool chains, and workflows—an expensive, time-consuming process that doesn't scale.
Once deployed, agents rarely improve without human intervention. Business needs change; agents don't.
Thousands of combinations of prompts, tools, memory, and reasoning strategies—far beyond what humans can systematically explore.
more agent configurations exist than teams can manually evaluate in a sprint
of agent performance gains are left on the table due to limited architecture search
average time to redesign an underperforming agent without automated tooling
Our Solution
The meta agent automatically discovers, tests, and deploys better versions of your AI agents—turning static systems into continuously improving ones.
Live optimization run
How It Works
Our platform uses a meta-agent to iteratively generate, evaluate, and refine target agents at the code level—not just the prompt level.
The meta-agent creates diverse agent architectures—varying prompts, tool configurations, memory systems, and reasoning strategies.
Each variant is benchmarked on production metrics: accuracy, cost, latency, and reliability across your actual workloads.
Top-performing designs are selected and become the foundation for the next generation—building an evolving archive of strategies.
Winners are automatically deployed. The loop continues, compounding improvements as new data and tasks become available.
Key Capabilities
Purpose-built capabilities that transform agent development from manual engineering into continuous automated optimization.
Your agents improve automatically as new data, tasks, and tools become available. No manual re-engineering required. Bliss Labs handles the full optimization lifecycle—from variant generation to deployment—continuously, in the background.
Learn more →Performance over time
We explore the full design space of AI agents—finding the right structure for your specific tasks and performance targets.
Agents are optimized for metrics that actually matter to your business—not proxy metrics or subjective judgments.
Early Access
We're onboarding a small group of design partners. If your team is deploying AI agents and wants to explore automated optimization, we'd love to talk.