Agent runtime
Structured planning, action state, retries, tool selection, model routing, and failure-safe execution.
Kernolix is an AI agent development company focused on production behavior: structured plans, limited tool permissions, human approval for consequential actions, persistent run state, and evaluation against completed work.
Every engagement connects the model to a defined workflow, narrow tools, visible risk boundaries, and measurable completion criteria.
Structured planning, action state, retries, tool selection, model routing, and failure-safe execution.
Expose narrow read and write operations with explicit scopes, validation, and approval requirements.
Pause consequential actions for review while letting safe research and preparation continue.
Measure evidence quality, completion, corrections, tool errors, latency, and cost—not just response fluency.
The best first workflow is frequent, evidence-based, and measurable, with a clear owner and an explicit boundary for external actions.
Specify the goal, tools, allowed actions, risk boundary, evidence needs, and completion criteria.
Build the state, prompts, tools, guardrails, approval flow, persistence, and observability.
Run representative tasks, inspect failures, measure corrections, and increase autonomy only when results justify it.
It builds software agents that can plan and use tools within defined permissions, while managing state, approvals, errors, and evaluation around the model.
Autonomy is scoped by workflow risk. Safe preparation can run automatically, while consequential external writes remain visible and approval-gated.
Yes, when the required APIs, OAuth permissions, webhooks, or internal interfaces are available and configured with narrow access.
We will map the work, identify the risk boundary, and show what a controlled production pilot should prove.