Industry solution
IT Services
Replace the repetitive coordination, triage, and knowledge retrieval load on your delivery and ops teams with role-defined AI employees — not generic automation tools.
Powered by OpenClaw + NVIDIA NemoClaw. Delivered by Agentex.
Best-fit buyer
CTO, Head of Delivery, Head of Support, or Operations leader with SLA pressure and delivery coordination burden.
Where this fits
Best fit for IT services firms where revenue depends on response speed, QA consistency, internal knowledge access, and clean escalation handling across multiple teams.
Where the workflow breaks today
- L1 and L2 support queues move too slowly — escalations lose context and ownership gets fuzzy across handoffs.
- QA cycles are slow because engineers run tests manually, file tickets manually, and chase results manually.
- Client onboarding depends on fragmented human handoffs between delivery, support, and operations.
- High-value engineers spend time on repetitive updates, routing, and knowledge retrieval instead of delivery work.
What we can deploy first
- AI QA Employee — reads Jira tickets, runs test cases, files GitHub PRs, notifies the team. Acts like a QA engineer, on every sprint, without scheduling.
- AI Delivery Ops Employee — tracks sprint progress, chases blockers, sends client updates, and keeps delivery running without a coordinator manually following up.
- AI L1 Support Employee — triages tickets from WhatsApp/Telegram, auto-resolves known issues, escalates with full context. Handles volume your L2 team should not see.
- AI Knowledge Employee — answers team queries across SOPs, runbooks, and internal docs. Every engineer gets instant access to everything the org knows.
AI Employee Catalogue
Roles we deploy for IT Services
Each role is deployed end-to-end — integrated, policy-governed, and live in 2 weeks.
AI Employee
AI QA Employee
Reads Jira tickets, writes test cases, runs the test suite, files bug reports, pushes QA summaries to Slack, and escalates regressions with context.
Integrates with
Replaces 1 full-time QA engineer — hours saved from day one
AI Employee
AI Dev Employee
Picks scoped Jira tickets, writes code within a defined module, opens pull requests with explanations, responds to review comments, and closes tickets on merge.
Integrates with
Replaces 1 junior developer for defined-scope repetitive implementation work
AI Employee
AI Delivery Ops Employee
Monitors project health across Jira boards, sends status summaries to stakeholders, follows up on overdue tasks, updates delivery tracker sheets, and escalates blockers.
Integrates with
Saves 8–12 hours/week of project manager time on status-chasing and reporting
AI Employee
AI Support Triage Employee
Reads incoming support tickets, classifies by type and severity, resolves known issues from a playbook, updates CRM, sends first-response messages, and escalates genuine issues with full context.
Integrates with
Handles 70–80% of L1 ticket volume autonomously
Business outcome
Why the buyer cares
Agentex is not selling abstract AI experimentation. We are selling a faster, more controlled way to remove repetitive manual work from real enterprise operations.
- Reduce first-response time for routine support and internal service requests.
- Cut QA cycle time by automating test execution, result filing, and team notification.
- Free senior engineers from repetitive lookup, routing, and follow-up tasks.
- Create cleaner audit trail for SLA reviews, client reporting, and internal ops control.
Why Agentex
Why this works better than another pilot
- We define each AI employee by role — the QA agent acts like a QA engineer, not a generic bot.
- We deploy inside your infrastructure using OpenClaw + NemoClaw. No SaaS dependency, no data leaving your servers.
- We connect AI employees to your existing tools: Jira, GitHub, Slack, Telegram, WhatsApp, internal databases.
- We manage the workforce post-deployment — monitoring, tuning, and expanding to new roles on retainer.
Deployment discipline
How we keep rollout enterprise-safe
The first Sprint should land in a bounded workflow with a clear owner, clear escalation rules, and a deployment model that matches the organization’s operational constraints.
- Deployment runs on-prem inside your GCP, AWS, or bare-metal environment. No data leaves your servers.
- NemoClaw OpenShell policy files govern each AI employee's file access, network egress, and inference calls.
Sprint deliverables
What the buyer gets after 2 weeks
The Sprint is designed to produce a real deployment outcome, not a slide deck with vague recommendations. The buyer should leave with a live workflow, a clearer operating model, and enough signal to decide on the managed retainer.