Managed AI agents AI agents with a clear job, controlled access and an accountable operator.
Connect an agent to a defined workflow, give it only the access that workflow needs and make its actions reviewable. Z7 scopes the tools, model provider, data flow, evaluation and ongoing operations before deployment.
- Self-hosted sandboxes
- MCP tunnels, no inbound holes
- Guardrails and audit logging
- In business since 2012
What to decide before you begin
A useful engagement starts with a specific outcome and clear responsibilities.
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A useful, bounded job
Begin with a draft, research summary or internal knowledge task. Name the owner, allowed tools, prohibited actions and the point at which a person takes over.
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The model boundary is explicit
A self-hosted agent can still send selected content to a hosted model. Review every provider and connector path; choose local inference when your requirements call for it.
Explore local inference -
Prove control of actions
Test denied access, misleading documents, failed tools and duplicate requests. Require human approval for consequential actions, plus a stop control and a recovery procedure.
Two capabilities that remove the enterprise blockers
These address the two structural reasons enterprises rejected agentic AI: data egress and network exposure.
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Self-hosted sandboxes
Agent orchestration and your data stay in your tenancy: execution, file storage, and tool calls run inside your perimeter, behind your existing DLP and logging. Model inference runs with the model provider, Anthropic, under its enterprise terms, so the text the model needs for each step goes there.
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MCP tunnels
Your Claude agent reaches MCP servers inside your private network with no public exposure: an outbound tunnel, no inbound holes. The integration that once required weeks of security review becomes a documented configuration step, with the review built in.
Anthropic Claude
Built on automation
Z7 runs about 80 percent of its own work automated, on systems we built. The agents we build for you come from the same practice: connected to the tools you already use, with a person approving what matters and every step on the record.
From one use case to agents in production
Z7 ships production MCP servers and operates its own AI infrastructure to deliver agents that do real work and survive in production.
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Agent use case workshop
A one-week engagement to identify your highest-impact agentic use case. Output: a one-pager defining the agent's role, tools, success metrics, and deployment architecture.
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Pilot agent development
In four to eight weeks we design, build, and deploy a production pilot: persona engineering, tool inventory, sandbox configuration, MCP tunnel setup, guardrails, audit logging, and an end-user interface.
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Production agent deployment
Once a pilot proves out, we deploy across the business: multi-tenant architecture, high-availability sandbox infrastructure, cost optimization, and operations with response targets published in our SLA.
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Managed agent operations
A monthly retainer: 24/7 monitoring, model version management, prompt-engineering refresh as Claude evolves, policy updates, and governance audits.
The integrations and the governance around every agent
Two more offerings make an agent safe to connect and defensible to run.
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Custom MCP server engineering
Integrations
The integrations the agent needs that do not exist off the shelf. Z7 has shipped production MCP servers for GoHighLevel, WordPress, and customer systems.
- CRM and ERP
- EHR and LMS
- Billing and ticketing
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Agent governance framework
Governance
A defensible program for the agents you run.
- Acceptable use
- Audit logging and retention
- An incident-response playbook
- Regulatory mapping: FERPA, HIPAA, CMMC, PCI, GLBA
- Board-ready reporting
Agents we have deployed or designed
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Compliance pre-screening
Reads contracts and RFPs against your baseline, flags issues, drafts redlines, and escalates, fully audit-logged.
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Sales qualification
Enriches inbound leads, scores against your ICP, drafts outreach, and hands off at the qualified-meeting threshold.
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Code review
Reviews pull requests against your standards and security policies, posts inline comments, and escalates security findings.
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Customer support tier-zero
Handles common tickets end to end and escalates complex ones with full context, inside your existing helpdesk.
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Document QA
Answers questions about your knowledge base with cited sources, across SharePoint, Drive, or custom repositories via MCP.
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Security analyst augmentation
Triages alerts, correlates events, drafts incident write-ups, and queries SIEM and EDR via MCP servers.
- In business since 2012
- Production MCP servers shipped
- Z7 operates its own AI infrastructure
- 24/7 agent monitoring
- Response targets published in our SLA
Managed agent questions
Decisions, scope and operating responsibilities.
What is a managed AI agent?
An agent that does real work, day after day, against your real data and your real policies. Z7 builds it, configures the sandbox, integrates the tools, hardens it, deploys it, and operates it.
Does our confidential data leave our environment?
That depends on the approved model and workflow. Hosted inference sends selected prompts and context to the provider, even when agent execution and storage are self-hosted. We document what crosses that boundary and review the applicable product terms and retention settings.
Do we have to open our network to the agent?
No. MCP tunnels let the agent reach MCP servers inside your private network through an outbound tunnel, with no inbound holes and no public exposure.
How long does a pilot take?
Four to eight weeks. We design, build, and deploy a production pilot, from tool inventory and sandbox configuration to guardrails, audit logging, and an end-user interface.
What do managed agent operations include?
24/7 monitoring, model version management, prompt-engineering refresh as Claude evolves, policy updates, and governance audits, on a monthly retainer.
Can an agent send messages or change records automatically?
Only where the agreed workflow explicitly permits it. We separate reading, drafting and executing, define approval points and log the allowed actions. A successful demo does not authorize unrestricted access.
What is measured in a pilot?
Task accuracy, evidence quality, tool failures, review time, latency and actual provider and operating cost. Acceptance thresholds are agreed for your workflow before the pilot is evaluated.
Put a managed AI agent to work.
We build the agent, configure the sandbox, integrate the tools, harden it, deploy it, and operate it. Let us find your highest-impact use case.