An agent's ceiling is set by what it learned from the expert who trained it.
Expert experience is the critical input
A general model knows language, but not necessarily your discipline. What sets professionals apart lives outside any document: the methods for framing a problem, the demonstrations of how work is actually done, and the feedback on what counts as a good result.
Songta treats these three as the raw material of growth. Experts raise agents the way they would train an apprentice—showing how to analyze a problem, demonstrating the professional tools, and judging the outcome.
RL and Memory turn feedback into experience
Feedback that stays in a chat window has to be re-taught on the next task. Songta uses reinforcement learning to improve the agent from feedback, and Memory to distill validated experience into reusable memory and skills.
Depositing experience works both ways: experts deliberately write their methods down as skills, and the agent keeps learning on its own from real tasks. That is test-time learning—capability compounds in use instead of freezing at the shipped version.
Growth is a process, not a one-time setup
A newly raised agent has clear but narrow capability boundaries. Each project it joins and each piece of feedback it receives tests and widens those boundaries.
By the time an agent joins project collaboration, it is no longer a generic assistant—it is a professional role raised by a specific expert. That is what makes collaboration possible.
This article describes verifiable current workflows and does not present planned capabilities as shipped features.