I'm Mythri.
You can call me My3.

Context

Long-running operational work does not always arrive with complete information. A useful agent needs to investigate carefully, preserve its place while it waits, and return to a case without losing the thread. Continuum explores that challenge through a financial-reconciliation reference workflow built with fictional data.

What I built

I built Continuum on Google ADK as a framework for durable, multi-step agents. It turns incoming work into a case, gathers and checks evidence, pauses when more information is needed, resumes with context, and prepares a recommendation for human review. Accepted corrections can inform future runs through an evaluation gate.

Engineering decisions

I kept investigation and verification as distinct steps, made waiting and resuming part of the workflow, and left approval with a person. The local reference implementation uses SQLite. Its production architecture is designed for Google Cloud, with managed services supporting scheduled work, durable state, incoming evidence, and model access.

Production success targets

• <1% materially incorrect recommendations among cases presented as ready for review
• 0 duplicate or unauthorized actions
• 95% of recommendations accepted without substantive analyst correction

How I would measure them

Compare recommendations with analyst decisions, measure corrections against all reviewed recommendations, and reconcile every proposed action with its final recorded outcome. Review-ready time and safe escalation rates show whether the framework makes the work more useful without pressuring the agent to guess.

Evaluation

The reference workflow is exercised on synthetic reconciliation cases, including incomplete evidence, ambiguous findings, interruptions, and recovery. Production targets are intentionally separated from those evaluation results; the targets require measurement against real operating baselines.