Deployed on-prem · trained on your data
Infrastructure for self-evolving harness / model
thetalab builds a custom agent for your exact use case, from insurance processing to customer care. It runs on your own infrastructure and gets more reliable the more it runs, so your data never leaves and compliance is never a question.
01
Observe
Capture every agent run as a replayable trace.
02
Simulate
Rebuild the hard cases as RL environments to train in.
03
Train
Distill proven behavior into a custom model you own.
Why thetalab
Self-improvement is infrastructure, not a prompt.
Agents get better when every production run feeds back into training. thetalab is that loop: observe, simulate, and train on your own infrastructure, closing after every release.
Start from evidence
Every agent run becomes a trace you can replay, score, and turn into training signal, not a guess.
Practice the hard cases
Failures, timeouts, tool errors, and policy checks become repeatable RL tasks in a safe environment.
Own the model
Proven behavior distills into a workflow-specific model: lower cost, controlled, and improving on a live loop.
01 · Observe · obsrv.tech
Every agent run has evidence.
Obsrv.tech is the substrate every self-improving loop runs on. It captures live agent executions, makes failures replayable, and gives you the data for debugging, evals, and retraining.
Our platform
Live executions, replayable forever.
Trace every run
Messages, tools, model, user, latency, tokens, cost, and metadata.
Replay failures
See the exact step where the workflow drifted, looped, or broke policy.
Create eval sets
Turn real production failures into repeatable checks before release.
Monitor regressions
Keep watch on high-volume workflows after the model or workflow changes.
02 · Simulate · RL environments
Turn your hardest cases into a training gym.
Instead of hoping a generic model understands your domain, thetalab gives your agent a safe place to practice the exact task, with the same tools, the same rules, and measurable rewards on every attempt.
Company RL environment
One workflow, repeatable thousands of times.
Source
Production traces and failure cases
Start from the workflows that already cost time: retries, escalations, bad tool calls, policy misses, and expensive human review.
Environment
A private training version of the workflow
We mirror the tools, forms, data states, permissions, edge cases, and handoff rules your agent must handle safely.
Score
Deterministic checks for each run
Every attempt is scored for correct state changes, policy adherence, completion quality, cost, and safe escalation.
03 · Train · custom models
Distill proven behavior into a model you own.
thetalab trains a custom model on your RL environment, so proven agent behavior becomes cheaper, more consistent, and yours to control, not a generic API call you rent.
Workflow-trained model
Small where it should be small. Reliable where it must be reliable.
Lower cost per run
Move repeat workflows off expensive general models once the behavior is proven.
Company-specific behavior
Train on your policies, approval paths, exceptions, and internal workflow states.
Controlled rollout
Evaluate, monitor, and improve the model with the same evidence loop after launch.
Blog(6)
Bring one agent. Leave with a self-improving loop.
We'll trace it, rebuild the hard cases as an RL environment, and show whether a custom model should own the repeat work.