Syntonyl Agents

An AI agent is useful when it can do more than talk: look something up, fill in a form, call an API, and hand the result to the next step. That same ability is what makes agents risky. Without permissions, budgets, and review, a demo that impresses in a meeting can fail unpredictably in production.

Syntonyl Agents is the governed runtime we use to put agents into real workflows. It includes a tool registry that defines exactly what an agent may call, memory and retrieval over your knowledge base, guardrails enforced through role prompts and policy checks, and explicit human-in-the-loop steps for higher-risk actions.

Production concerns are part of the design. Cost and latency budgets keep spend predictable, offline jobs run with retries, tool use is sandboxed, and every invocation leaves an audit trail. Evaluations measure whether the agent is doing the job correctly before and after release. The same approach powers IVOKER, our own AI operator.

What’s included

• Task and workflow orchestration
• Tool registry with scoped permissions
• Human approval steps for higher-risk actions
• Role prompts and policy checks
• Retrieval over your knowledge base
• Cost and latency budgets
• Offline jobs, retries, and sandboxed tool use
• Audit trails, logs, and evaluation suites

A good fit when

• The task needs actions across tools, not just answers.
• You can define which actions are safe and which need approval.
• You care about cost, latency, and an audit trail.
• You have examples of the work done well.