Itential

We’re building new products, and we’re building our team. 🚀

At Itential, we're looking for people to join our team who like solving hard problems and turning big ideas into things that actually work.

We’re hiring across several teams in the US and UK, with a number of remote opportunities available.

👉 See where you could fit in. Explore open roles: https://bit.ly/4hko25r

Know someone who should be on our radar? Tag them below.

One over-permissioned AI agent can turn a bad decision into a production incident at machine speed.

That makes the boundaries set before runtime critical. Teams need to decide which tools an agent can call, which actions require approval, and which systems remain off-limits. The model can still reason dynamically, but only within the authority humans have explicitly granted.

Itential CTO Chris Wade shares this perspective in a recent Forbes Expert Panel on the safeguards enterprises need before connecting AI agents to critical systems.

Read the full article: https://bit.ly/3TLJQit

A lot of buzz at the Georgia Tech Career Fair yesterday!⚡🐝

We had a great time meeting students, talking all things engineering, automation, and AI, and sharing a little about what it’s like to build at Itential.

Our co-op program gives Georgia Tech students the chance to take an active role shipping products from day 1 working alongside our teams to contribute to real projects during the semester.

If you missed us at the event, reach out to our team to learn more about our Co-Op program.

Thanks to everyone who stopped by the booth. See you next Spring!

AI is changing the kinds of workloads Kubernetes is being asked to run, and that puts new pressure on the infrastructure, orchestration, and control planes underneath them.

On Monday, John Capobianco joins BMC’s Anthony Anter and Techstrong Group’s Michael Vizard for a live discussion on what teams need to think about as AI infrastructure continues to scale.

They’ll cover:

• GPU orchestration and what gets harder at scale
• Model deployment + inference patterns for production environments
• AI-powered operations and self-optimizing clusters
• The infrastructure and control requirements behind AI-native platforms

For platform engineers, SREs, architects, and infrastructure teams, this is a practical look at how AI is changing the operating model around Kubernetes, not just the workloads running on top of it.

📅Monday, September 21 | 1 PM ET

Register here: https://bit.ly/4isq6ut

AI agents are getting smarter fast. But intelligence alone doesn’t earn them access to live network infrastructure.

John Capobianco joined Swapnil Bhartiya on TFiR to unpack what it actually takes to bring agents into network operations without giving up control.

They get into:
• Why read-only tasks are a practical starting point for agentic automation
• How FlowAI separates AI reasoning from deterministic ex*****on
• Why build-time tool selection is the key to controlled access + limiting blast radius
• Real use cases spanning ticket triage, source-of-truth reconciliation, and port turn-up

The conversation gets at the bigger challenge with agentic operations: giving AI enough access to be useful while keeping the controls, visibility, and auditability network teams need in production.

Watch the full conversation: https://bit.ly/4gZAo4d

A major North American airline is bringing thousands of network devices back in-house while building the operating model to manage them with a lean team.

With Itential, the airline is replacing manual, overnight change processes with governed automation and laying the foundation for AI agents to safely operate across production infrastructure.

The expected impact:
✅ 100x more devices managed by the internal team
✅ 95% faster routine changes
✅ 80% fewer after-hours change windows
✅ 60% less engineering time spent on repetitive change work
✅ $3.2M+ in projected annual cost avoidance

And every change, whether initiated by an engineer, workflow, or AI agent, runs through the same RBAC, approvals, and audit model.

See how the airline is scaling operations in-house today while building toward an AI-ready future. 👇 https://bit.ly/4xWfV5G

Building an AI agent that works is just the tip of the iceberg.

Below the surface is everything that determines whether it can actually operate safely in production: scale, failure handling, governance, approvals, auditability, and the amount of human-in-the-loop oversight.

Holly Holcomb breaks down what that foundation looks like, including:
✅ Building governance + approvals into ex*****on
✅ Adjusting human oversight based on risk + confidence
✅ Keeping every agent-driven change auditable
✅ Creating an operating model teams can trust to adopt

See why operating agents safely depends on the tools, controls, and governance built around them, and what that actually looks like in practice.

Check out the full blog:

The Iceberg Effect: What It Actually Takes to Trust an AI Agent in Production A working agent is easy to demo. Getting it into production, with governed change management, audit trails, and human-in-the-loop controls, is the real challenge.

What does agentic change management look like when the network is highly regulated and every action has to be defensible?

For a global managed service provider, it meant putting AI to work across BGP change operations while keeping validation, approvals, audit trails, and human oversight intact.

The team built four specialized FlowAgents to handle:
1️⃣ BGP configuration validation
2️⃣ ServiceNow change request creation
3️⃣ Duplicate change detection
4️⃣Scheduled change ex*****on

In testing, a FlowAgent caught a configuration error and blocked ex*****on even after the change had already been approved upstream.

This is where agents start to change operations: not just assisting with a change, but carrying it through validation, coordination, and ex*****on under governance.

See how they built it: https://bit.ly/4rd9J7s