A model stops learning the day it ships. Your agent shouldn't.
Sikaru improves the whole agent, context, harness, and model, not just the prompt, and proves every gain, run over run.
Fig. 01 · one agent, 412 production runs. Every miss became the change that made the next run better.
A bigger model plateaus. An agent that learns from real work compounds.
That is the bet Sikaru is built on: continual learning as managed infrastructure, improving context, harness, and the model together, not the single axis everyone else stops at.
Stay on outcomes. Send your traces, what the agent should care about, and what good looks like. Sikaru runs the durable managed agent, the evaluations, and the improvement as infrastructure.
Start from scratch, or move an agent you already operate.
Corrections, feedback, failures, recovery, traces. Sikaru learns from all of them.
What good looks like is optional. Sikaru can infer it from real work and raise it.
The agent runtime, kept running and maintained for you.
Sikaru builds the bar and raises it over time, not just checks you write.
Across context, harness, and model, on equal footing.
Nothing is retrained to start. Sikaru works the run you already have: it finds what went wrong, changes the one thing that caused it, and proves the next run is better before anything ships.
Fig. 02 · one miss, six steps, a better next run.
Most tools stop at the prompt. Sikaru improves the context, the harness, and the model on equal footing, and only where the work shows it should.
What the agent knows and is told.
How the agent runs the work.
The model's own behavior, shaped from your work.
Fig. 03 · context, harness, and model, improved on equal footing.
Improvement you cannot see is not improvement you can trust. Every change keeps its reason, the check it had to pass, and the name of the person who approved it.
Fig. 04 · one learning release, from miss to shipped.
Your work stays in your workspace. We do not train foundation models on it.
Nothing reaches the agent until a check passes and a person approves.
No retraining to begin. You improve the agent you already run.
Sikaru is managed continual learning infrastructure for AI agents. You connect an agent you already run or build a new one, and Sikaru improves it from real work across context, harness, and the model itself.
Continual learning means improving a deployed agent from its own production work instead of leaving it frozen at training. Sikaru turns misses, user corrections, feedback, and failures into the change that makes the next run better.
Managed agents have their live behavior, safety checks, and improvement handled by Sikaru. You define what good looks like, or let Sikaru infer it, and Sikaru keeps the agent running and improving.
Sikaru catches a signal such as a miss or a correction, finds the pattern, makes the smallest change to the layer that caused it, checks the change against your bar, and makes it live after review so the next run is better.
Both. Sikaru improves the whole agent on equal footing: context, harness, and the model itself, and only where the work shows it should.
No. You can start a new managed agent from scratch and it improves from its first runs. Historical examples help but are not required.
Observability shows what happened and evaluation tools score it. Sikaru turns that signal into reviewed improvements across the agent, so future work gets better from past work.
Create one from scratch, or move one you already run. Sikaru evaluates it, raises the bar, and improves it from the first run. No dataset required.