The Agent Lifecycle with Marlo
Agents that learn autonomously
Observe agent behaviour
Capture complete agent trajectories, including actions, tool calls, reasoning steps, and outcomes. These trajectories are evaluated and rewarded, allowing the right behaviours to be reinforced and failures to be identified.
Full trajectory capture
Automatic evaluation
Reward signals
Failure detection
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About us
Marshmallo was born out of the frustration that agents cannot be trusted in production.
It's never been easier to build and ship agents but it's still painfully hard to make them reliably good and keep them getting better. Today, teams glue together observability dashboards, eval harnesses, and manual review processes.
Marshmallo's infrastructure enables agents to learn and improve autonomously in their production environment.
We add an autonomous learning loop to your existing agent. The platform records what the agent did from input to output, the tools called, interactions, and reasoning, and our reward system scores the agent's performance against the intended outcome, in its production environment. When the agent underperforms, our learning system converts the reward score and rationale into lessons that are automatically deployed back into the agent.
Marshmallo closes the loop between observability and improvement through four stages: we observe the full lifecycle of each task, evaluate each output with our LLM-as-judge scoring system, learn by converting scores and rationales into lessons, and deploy those lessons directly into the agent's context. Creating a dynamically evolving context, where we are able to learn what "good" means for your specific production environment at inference time.
Our learnings feed back continuously, so your agents get more reliable and cheaper over time. Instead of babysitting your agents, you can focus on what you do best: building your product.