Agents that stop failing on your specific tasks.
Not "smarter agents in general." Targeted optimization — tools, prompts, rules, policies — under your constraints, for your KPIs.
For development teams.
- → No manual prompt iteration
Forge iterates automatically. You define what "good" looks like (evals), it finds the configuration that gets there.
- → Full-stack agent optimization
Not just prompts — tools, rules, data access, routing. At L3, the optimizer creates new tools autonomously.
- → Every change visible and reversible
Git-like diffs of the full agent structure. Score delta per change. Roll back anything.
- → Automatic re-optimization on degradation
When production scores drop, retraining triggers. No human in the loop unless you want one.
Five strategies — each addresses a different class of weakness.
Agent failures aren't uniform. Some are prompt-level mistakes, some are architectural. A single training strategy can't address both.
For regulated domains where every change requires human review before it enters the loop.
Addresses failures the agent can diagnose itself — wrong tool selection, incomplete reasoning, missed edge cases.
Parallel variants explore a large search space. Finds optima that sequential iteration misses.
Isolates coupled dimensions. Improving safety often degrades throughput — one dimension at a time prevents oscillation.
When the bottleneck isn't the prompt. Tool design, data access patterns, and workflow structure all affect quality.
Depth levels — because the bottleneck changes.
Early iterations fix prompt-level issues (L0). As quality improves, the bottleneck shifts to tool design (L2), workflow structure (L3), or multi-agent coordination (L5). Depth levels ensure the trainer reaches the actual constraint.
The monitoring-to-training loop closes automatically.
Monitoring and training share the same evaluation infrastructure. Drift detection already has the eval chain, the task suite, and the baseline — retraining is another iteration of the same loop.
For the business.
Automated cycles from $4.91 (example run). Instead of 3 months of prompt engineering, Forge optimizes to your targets automatically.
Out-of-sample testing on tasks the agent hasn't seen. You know it works beyond training data before you ship.
When the world changes — new policies, new tools, new data — retraining triggers automatically. The agent adapts.
Turn evaluation scores into better agents.
Iterative improvement with visible results every cycle.