Only 16% of an engineer's time is spent writing code.

That's the number Adam Witanowski keeps coming back to. Hand your engineers the best coding agent on the market and individuals accelerate 120% to 400%. But you've optimised 16% of the pipeline. Measured end to end, he saw between zero and 40% velocity gain, with everything bottlenecking at the PR.

So the sharper question becomes:

If the coding agents aren't the constraint anymore, what is?

In this episode of Agents After Dark, Matt Doughty sits down with Adam Witanowski, AI architect behind one of Australia's most comprehensive enterprise AI transformations, to break down what it takes to move an organisation up the agentic maturity curve.

Together they discuss:

• Why rolling out a coding copilot and calling the job done just bloats the middle of your delivery pipeline
• The harness he built across the full SDLC, and why rework and incidents beat adoption and token spend as metrics every time
• His audit of 10,000 historical PRs: roughly a third could have been automated, and if you're not ready to automate any of yours, your maturity is lower than you think
• Governor: policy as code injected into coding agents at invocation time and reasserted on CI, so cyber, legal and compliance shape every build without slowing the dev loop. Human in the loop, not human in the way
• Skynet: 3,600 repos ingested into a knowledge graph with decision history, so agents see the downstream impact of a change instead of breaking the team next door
• Why engagement went up while productivity went down, and what job anxiety and a weak front of funnel do to an AI rollout
• Winning over non-engineers: sit with the call centre, find the pain, and sell teams a solution rather than announcing their replacement

His test for where any organisation really sits: maturity isn't about the model. It's about how much you trust the agent when no one's watching it.

This conversation is a practical playbook for any engineering or AI leader trying to turn coding agents from an individual productivity toy into an organisational capability.

About Adam
Adam Witanowski is an AI Executive who spent the past year building one of Australia's most comprehensive enterprise AI transformations from the inside: 11 integrated agentic tools reshaping how engineering teams build software, reimagining the SDLC into an AI-DLC with full executive backing. He shipped 18 products to production in 12 months without writing a meaningful line of code himself.

He is now a consulting Chief AI Officer and keynote speaker on AI-powered engineering, with a background in bioinformatics and, formerly, the best job title in the industry: Chief Officer of Creative AI and Novel Engineering.

About Prefactor
Catch your agents' mistakes before they reach customers.

We evaluate every step your agents take, live. Mistakes get caught as they happen. Not in a Slack thread at 1am on a Tuesday.
Learn more at prefactor.ai

Would Toyota release a car without a seatbelt?

That's the question Sara Abak keeps coming back to. No carmaker would ship a vehicle without safety features, yet the most powerful software ever built ships with no regulation at all. Every single cyber leader she speaks to is worried about frontier AI models.

So the sharper question becomes:

How do you secure the enterprise when AI is moving faster than the rules around it?

In this episode of Agents After Dark, Matt Doughty sits down with Sara Abak, CISO across energy and critical infrastructure, to break down what security leadership actually looks like in the AI era.

Together they discuss:

• Her case for regulating frontier AI like any other product that can cause harm at scale, and why unsafe releases should be paused, not innovation blocked
• How she turns around security cultures in complex organisations: anchor in what the business actually does, because you can't protect what you can't see
• Why CISOs get the "department of no" reputation, and how building trust at board level beats laying down the law
• Why AI features in your ERP or board paper platform stay switched off until they pass a risk assessment
• Shadow AI in practice: locking down what staff can paste into public AI tools, coaching at the point of upload, and quarterly behaviour reporting so decisions are anchored in data and facts
• Why standalone responsible AI programs rarely get funded, and why anchoring AI work inside an existing project with legal, HR, and IT at the table is what actually moves things forward
• How a clinical psychology degree shapes the way she reads the room, wins support, and accepts what she can't change

Her three-part answer to frontier AI risk: regulation, layered foundational controls so you're never the easy target, and holding your vendors accountable. And one rule that applies to all of it: never waste a good incident.

This conversation is a practical guide to enterprise AI security for any leader trying to enable adoption safely while the technology outruns the rulebook.

About Sara
Sara Abak is a Chief Information Security Officer with over 20 years in information security and risk, currently CISO & Industry Leader (Energy & Water) at Grampians Wimmera Mallee Water and Founder of Orca One, a fractional CISO advisory.

Previously CISO and Chief Privacy Officer at Intellihub, she has secured critical infrastructure across Australia and New Zealand spanning IT and OT, led ISO 27001 certification with zero non-conformances, and was recognised as a Most Influential & Top 100 Cyber Security Leader across ANZ in 2025. She is a certified AI governance leader and ISO/IEC 42001 Lead Implementer.

About Prefactor
Catch your agents' mistakes before they reach customers.

We evaluate every step your agents take, live. Mistakes get caught as they happen. Not in a Slack thread at 1am on a Tuesday.

Learn more at prefactor.ai

What happens when your customer is an AI agent, not a person?

For 25 years InfoTrack has sold authoritative legal and property data to people clicking through interfaces. Now it is making those same services callable by AI agents using the Model Context Protocol (MCP). That shift raises a sharper question:

How do you actually run MCP in production inside a regulated enterprise?

In this episode of Agents After Dark, Matt Doughty sits down with Ajay Kumar, Head of MCP Services at InfoTrack, to break down what it takes to move Model Context Protocol from experiment to production.

Together they discuss:

• Why InfoTrack stood up a dedicated MCP business unit instead of treating AI as an experiment
• How an enterprise MCP gateway works in practice: a central registry, a search-and-invoke pattern, and vectorised tools that cut token costs across hundreds of services
• Why human-in-the-loop elicitation matters the moment an agent can spend money, and why most major providers still don't support it out of the box
• Why hallucination is an agent problem, not an MCP problem, since MCP itself is deterministic
• How MCP security is evolving after incidents like the Aura health breach
• The shift in customer mindset from "what is MCP?" to "make my agent more productive"
• Why roughly 62% of companies are experimenting with agents while only 28% are scaling, and what separates the two

As agents move from assistants to actors that order, transact, and execute, the businesses that win may be the ones whose products are built to be consumed by software.

This conversation is a practical guide to running Model Context Protocol in production, covering architecture, security, and go-to-market, for any enterprise starting its own MCP journey.

About Ajay
Ajay Kumar is Head of MCP Services at InfoTrack, where he leads the company's Model Context Protocol business unit and shapes how AI moves from experimentation into core production workflows. With over 17 years across banking, prop-tech, insurance, and government, Ajay brings an engineering-first perspective on identity, architecture, and enterprise-scale delivery, and on what it actually takes to make agents and MCP work in regulated environments.

About Prefactor
Prefactor helps enterprises trust AI in production.

As organisations deploy more AI agents, maintaining visibility into performance, risk, and operational quality becomes increasingly difficult.

Prefactor gives engineering, product, and security teams a single platform to monitor AI systems, evaluate outcomes, identify risks, and take action when things go wrong.

Learn more at prefactor.ai