Production AI agents your business can actually deploy.
Autonomous Agents apply Enterprise Context Engineering to specific operational roles—giving teams production-ready AI agents that operate inside your systems, work within defined boundaries, and scale high-value knowledge work with human oversight.
Discovery → role definition + guardrails → first agent live in 4–6 weeks
Built for teams with repeatable knowledge work, internal bottlenecks, and enterprise requirements around security, compliance, and operational trust.
Deployed in your cloud · 20+ years engineering leadership · Human-in-the-loop by default
Why enterprise agent pilots stall.
Production agents require context, controls, and operational trust—not just agent demos.
Pilots never reach production
Your team has run demos for months. Nothing is actually operating in the business. Leadership wants leverage; you have screenshots.
Generic agents are a liability
Off-the-shelf agents hallucinate, break SLAs, and expose data. Procurement, legal, and security block anything you cannot control.
Your context is scattered
CRM, docs, email, Slack, tickets, tribal knowledge. An agent with no context produces generic output — which is worse than no output.
Humans stay in the loop forever
You do not want an autonomous AI. You want an agent that drafts, analyzes, and queues — so humans can focus on judgement.
What an Autonomous Agent engagement delivers
A production-ready AI agent built around a specific operational role—measurable, secure, and ready to scale. Trained on your context. Deployed in your tools. Gated by your team.
- Agent scoped to a single operational role (ops, sales, CS, support, finance ops)
- Built on your systems, business context, and operational decision logic through the Enterprise Context Engineering layer
- Operates inside your existing tools (Slack, email, CRM, ticketing, BI, internal apps)
- Human oversight for customer-facing, financial, or irreversible actions
- Evaluation dashboard: what the agent drafted, what humans approved, what changed
- Deployed in your cloud with SOC 2-ready audit logging from day one
How Autonomous Agent engagements start
A focused implementation path designed to define the right role, controls, and production path before build begins.
Discovery call
Walk through where the team is most bottlenecked, which job would pay back fastest, and what compliance boundaries the agent has to live inside.
A prioritized list of candidate agent jobs with rough ROI.
Role + guardrails scope
Two-week scoping: what the agent will draft, decide, and execute; what always requires a human; what data it needs access to; what success looks like.
A role charter your legal and security teams can sign off on.
Build and go live
Agent built, integrated with your context layer, and evaluated against your standard. Weekly calibration with the business owner until outputs match. Ships live with full human-in-the-loop approval.
A production agent operating inside your business, not on a slide.
Which operational job would pay back fastest if an agent handled it tomorrow?
Autonomous Agents in practice
See how metacto transformed a repetitive operational bottleneck into a production AI agent with measurable business impact.
Mid-market B2B SaaS, 220 employees
The problem
Customer success team was drowning in manual renewal-prep work. Each CSM was spending 6+ hours per renewal pulling data from CRM, usage logs, support tickets, and calls. Two prior 'AI copilot' pilots had stalled — neither produced outputs the team trusted.
The outcome
Enterprise Agent scoped to renewal briefs: pulls usage, health, support history, and call context into a one-page brief the CSM reviews and approves. Approved briefs auto-populate the renewal playbook. Human signs every outbound.
Common questions
How is this different from off-the-shelf agent platforms?
Those ship horizontal capabilities; you still have to scope the job, wire the context, and build the guardrails. We do that for you, for one specific role, to a production standard your compliance team will actually approve.
How autonomous are these agents?
These are production AI agents with defined operating boundaries and human oversight for material actions.
The goal is operational leverage—not uncontrolled autonomy.
Which operational roles fit best?
Renewal prep, account research, proposal drafting, support triage, ops reporting, finance reconciliation prep, internal-knowledge lookup. The scoping call narrows to the role with the best payback in your business.
What does it cost?
First production agent engagements typically range from $200–400k depending on complexity, integrations, compliance requirements, and evaluation standards.
Where does it run?
Your cloud (AWS, GCP, or Azure). Your data, your auth, your perimeter. The agent is a private deployment, not a shared SaaS.
Compliance and audit?
Every agent action is logged — input, reasoning, output, approver, timestamp. SOC 2-ready audit trail. We've deployed under enterprise and regulated environments.
Is this the right fit?
Good fit
- Team has a repeatable, high-volume job costing senior-person hours
- Pilots have stalled and you need something that actually ships
- Compliance, legal, or security blocks generic AI tools today
- Comfortable with human-in-the-loop — you do not want a fully autonomous agent
Not a fit
- Looking for a fully autonomous AI (we do not build those)
- One-off experimentation without a sponsor or a real operational job to solve
- Budget below ~$150k
- No appetite for putting a system into production alongside humans
Scope your first production agent
30 minutes with a CTO to assess the operational role, systems involved, implementation constraints, and likely business impact.