managed agents by instruolabs
Managed agents that work
in production.
Private AI agents deployed and managed inside your workflows — connected to your tools, documents, codebases, and communication channels.
the problem
The agent works in the demo.
In production, it breaks.
Value is not the agent alone. It is the complete delivery stack, and in production the rest is missing:
The gap between experiment and operational process is an engineering problem, not a model problem.
engagements
How we work together.
Workflow Audit
Process map, recommended agent workflows, risks and approval gates, architecture design, cost and maintenance estimate. Know exactly what to build before you build it.
Managed Agents Sprint
Runtime setup, hosting and configuration, knowledge base, integrations, approval flow, documentation, and 2-4 weeks of iteration. Working managed agents, deployed.
Managed Agents Retainer
Monitoring, fixes, new workflows, monthly quality and cost reports, governance and model updates. Managed agent operations that stay operational.
Most engagements start with an audit.
delivery
From process discovery to operations.
Qualification
We check the use case is real and worth automating — before anyone commits budget.
Discovery
We map the process, the data, and where it actually breaks today.
Architecture
We design the workflow, integrations, and approval gates on paper first.
Implementation
We build and deploy the runtime, wired into your systems.
Evaluation
We measure quality and cost against the process it replaces.
Handoff
You get documentation and a system you can run — then we stay.
get in touch
Ready to deploy?
Tell us about the workflow you want to automate.
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