AI agents, built and run in production.
Forward-deployed engineers build agents inside your operation. Kadre then operates them — monitored, evaluated, gated behind human approval, and repaired as models and data shift.
What this is
Managed AI agent services cover both building agents and running them in production. Kadre Labs embeds engineers in your operation to build agents against the real workflow, then operates them continuously — monitoring, evaluating, gating external actions behind human approval, and repairing them as models and data shift. A well-scoped first agent is typically live in production within one to two weeks.
Embed, build, production, run
The delivery model is forward-deployed: engineers work against your real workflow rather than a requirements document, and stay accountable once the agent is live.
Embed
Engineers work inside your operation against the actual workflow, not a written specification. What the agent needs to know, decide, and touch is established from how the work is really done.
Build
The agent is built against live data and real integrations from the start, with the approval gates and permissions designed in rather than retrofitted.
Production
A well-scoped first agent reaches production in one to two weeks. Multiple system integrations, security review for data access, or complex approval workflows extend that.
Run
Kadre operates the agent from there — monitoring, evaluation on a cadence, incident response, and continuous improvement as models and upstream data change.
What “we run it” actually includes
Most providers leave this undefined. These are the six things Kadre owns once an agent is in production.
Continuous evaluation
Agent output is scored against a reference set on an ongoing cadence, so quality degradation is detected rather than reported by a customer.
Human approval gates
External actions — outbound messages, CRM writes, anything customer-facing — wait in an approval queue enforced by the platform.
Full audit trail
Every agent action, tool call, and approval is recorded, so behaviour can be reconstructed after the fact rather than inferred.
Isolation by construction
An isolated instance per tenant, and inside each tenant every agent is bounded at the data layer so it cannot read another agent's records.
Drift and incident handling
Model updates, prompt regressions, and upstream data changes are treated as expected operating events with a defined owner and response path.
Agent estate management
Agents are inventoried, versioned, and mapped to the business metric each one is supposed to move — not run as a collection of scripts.
Our cloud or yours
The same artifact runs either way, so data residency is a deployment decision rather than a renegotiation.
Managed by Kadre
Agents run in Kadre's operated environment with an isolated instance per tenant. Fastest path to production, with no infrastructure obligation on your side.
Inside your cloud
The same system deployed single-tenant into your own cloud account, so agents and data stay inside your existing security boundary. A deployment choice, not a different product.
Managed AI agents, answered
What are managed AI agent services?
Managed AI agent services cover building AI agents and then operating them in production on the client's behalf. Building alone produces a working prototype; the managed component covers monitoring, ongoing evaluation, human approval workflows, incident response, and continuous improvement as models and data change. The distinction matters because agent quality degrades over time without active operation.
Who builds and operates AI agents for companies?
Kadre Labs builds and operates AI agents as a managed service for private equity firms, their portfolio companies, and growth-stage businesses. Forward-deployed engineers build agents inside the client's operation, and Kadre then runs them in production with monitoring, evaluation, governance, and human approval gates. Agents can run in Kadre's managed environment or inside the client's own cloud account.
Can AI agents be hosted in our own cloud account?
Yes. Kadre Labs offers two deployment shapes of the same system: a managed environment operated by Kadre, or a single-tenant deployment inside the client's own cloud account. The second keeps agents and data within the client's existing security boundary, which matters for regulated organizations and for sponsors with portfolio-wide data requirements.
How much do managed AI agent services cost?
Kadre Labs prices as a one-time build fee for the forward-deployed engagement, then a recurring monthly fee per agent in production. The build fee reflects workflow complexity and the number of systems the agent touches; the recurring fee covers operating the agent — monitoring, evaluation, approvals, and improvement. Pricing is quoted per engagement rather than published as a fixed rate.
How fast can an AI agent be deployed to production?
A well-scoped first agent can be live in production within one to two weeks using a forward-deployed model, where engineers build against the real workflow rather than gathering requirements first. Timelines extend when the agent requires several system integrations, when data access needs security review, or when approval workflows must be designed alongside the agent.
What business functions can AI agents handle?
Kadre Labs runs agent suites across sales development, marketing, finance operations, and back office workflows. Typical examples include an SDR agent that researches accounts, drafts outbound, and writes back to CRM with every send held behind human approval; marketing agents producing campaign content within brand guardrails; and finance agents handling reconciliation and reporting tasks.
How do you prevent AI agents from taking unwanted actions?
Actions that leave the system pass through an approval queue enforced by the platform rather than by prompt instructions. An agent can research, draft, and prepare work autonomously, but sending an email, writing to a CRM, or contacting a customer requires a human approval. Because the gate is architectural, it cannot be bypassed by a model behaving unexpectedly.
Put an agent into production in weeks
A 30-minute call to scope a first agent, the workflow it would own, and where it would run.