Technology, data and AI. Chief Technology Officer
AI for the CTO Office
The code takes hours to write. Getting it to production takes weeks.
Every team has AI coding tools now, and lead times have barely moved. The delay was never the typing. It’s the handoffs, the test queue, the reviews and the debt. We map the CTO Office the way your engineering teams actually deliver, then put agents where work waits.
The short answer
It speeds up software delivery without lowering the bar. Agents work beside engineers to draft requirements, write and review code, generate tests, analyse legacy code, keep dependencies patched and handle routine incidents.
Your engineers keep the architecture, the design and the final say on what ships. The agents take the waiting and the toil, so lead times fall and quality holds.
The mandate
Build what customers use, fast and safely. And keep the platform able to carry what comes next.
What the board measures you on
Deployment frequency
How often you ship
Lead time for changes
Idea to production
Change failure rate
Releases that break
Time to restore
When something breaks
Engineering productivity
Output per engineer
Cost per transaction
Platform cost per unit
The canvas of the CTO Office
Most AI for engineering stops at a coding assistant. The bigger value sits in the steps around the code, and in the standards that keep every team safe.
Canvas 1
What you run
The engine that builds the product.
Product engineering
Teams, roadmap and delivery of customer facing products.
Architecture and platforms
Core platforms, APIs and shared services.
Developer tooling
Environments, pipelines and developer experience.
Reliability
Monitoring, on call and incident response.
R&D and emerging technology
Prototypes and bets on new technology.
Canvas 2
What you answer for
The things the CEO, customers and auditors hold you to.
Engineering standards
Code quality, security by design and review practice.
AI in software delivery
Which AI tools are used, how and with what checks.
Open source and third party code
Licences and vulnerabilities in dependencies.
Platform cost
What each unit of business activity costs to run.
Where value leaks
None of them show up in a sprint report. They show up in lead time, failure rate and how tired your best engineers are.
01
Slow delivery from handoffs
Work waits between product, design, build, review and test.
Delivery agents. Requirement, coding, test and review agents work beside engineers so changes move with fewer waits.
Lead time for changes
Deployment frequency
02
Testing bottleneck
Regression testing holds up every release.
Test agents. Generate and maintain tests for every change and run the right regression set.
Test coverage
Escaped defects
03
Growing technical debt
Debt grows faster than teams can pay it down.
Code analysis agents. Map legacy code and dependencies, lock behaviour in with tests and refactor in small steps.
Debt ratio
Change failure rate
04
Uneven AI coding adoption
Every team uses different tools in different ways.
An agentic engineering playbook. Shared standards and tools, rolled out by principal engineers embedded in teams.
Adoption
Productivity per engineer
05
Incident toil
Engineers lose days to repeat incidents and on call noise.
Reliability agents. Triage alerts, find the likely cause and apply known fixes with approval.
Time to restore
Toil hours
06
Vulnerable dependencies
Open source updates pile up until an audit or an exploit forces them.
Dependency agents. Rank vulnerabilities by real exposure and open tested update requests for engineers to approve.
Critical vulnerabilities open
Time to patch
Process lifecycles
We start from your tools and pipelines. The orange steps are where an agent takes on the work, and engineers still approve everything that reaches production.
Idea to production
Every change
Requirement
Drafted and checked
Agent
Design
Build
Beside engineers
Agent
Review
First pass by agents
Agent
Test
Generated and run
Agent
Deploy
Monitor
Typical systems. GitHub, GitLab, Jira, CI/CD pipelines, Kubernetes
Reliability
Every alert
Monitor
Alert
Grouped and ranked
Agent
Respond
Known fixes with approval
Agent
Postmortem
Drafted from the timeline
Agent
Typical systems. Datadog, PagerDuty, Grafana
Modernisation
Every legacy service
Map
Code and dependencies
Agent
Lock in
Tests for current behaviour
Agent
Refactor
In small steps
Agent
Review
Release
Typical systems. Code repositories, static analysis, CI
Dependency health
Continuous
Scan
Every repository
Agent
Rank
By real exposure
Agent
Patch
Tested update requests
Agent
Approve
Verify
Typical systems. Dependabot, Snyk, software bill of materials tools
Agent intervention point
The CTO control tower
Not velocity charts per team. A live view of the DORA metrics, where work waits and what AI is really adding, with a brief on what needs your attention.
Illustrative
CTO control tower
Sample data to show the view. Not client results.
Deploys a week
46
▲ from 19
Lead time
2.7 days
▼ from 9
Failure rate
6%
▼ 3 pts
Time to restore
38 min
▼ 52%
AI assisted
41%
Of merged changes
Critical vulns
9
3 over 30 days
This week’s engineering brief
Waiting
Payments team changes wait 31 hours on average for review
Agent first pass proposed for low risk changes
Debt
Order service has 12% test coverage and 4 incidents this quarter
Test generation plan ready. Code analysis agent
Adoption
Three teams use AI on under 10% of changes
Principal engineer pairing scheduled
Fixed
Recurring cache incident now resolved by an agent in 4 minutes
Down from 50 minutes of on call time
Where a change waits, hours
Review
31
Test
22
Build
9
Deploy
2
Review and test take 83% of lead time. That is where agents go first.
Illustrative example. Figures are invented to show how the view works. In discovery we build it on your own data and KPIs.
Your industry changes the agenda
The canvas stays the same. What engineering builds, and the rules around it, does not. Here’s the question we hear most from technology leaders in each industry, and where agents help first.
CPG
How do we build digital products with a small engineering team?
Agentic delivery in one squad
FMCG
How do we maintain many market sites and apps without a big team?
Test generation and shared components
Retail
How do we ship faster without breaking checkout at peak?
Regression tests and reliability agents
D2C
How do we keep engineering lean as the product grows?
Delivery agents and AI coding standards
Automotive
How do we deliver vehicle software at the pace customers expect?
Test generation and traceability
Healthcare
How do we use AI in delivery within validation rules?
Traceable requirements and generated tests
Energy
How do we modernise grid and trading systems safely?
Legacy code analysis and lock in tests
Manufacturing
How do we update plant software without stopping lines?
Code analysis and staged releases
Logistics
How do we connect to hundreds of partner systems quickly?
Integration code and test agents
BFSI
How do we modernise core systems and meet DORA at once?
Modernisation agents and dependency health
B2B technology
How do we ship more without growing headcount at the same rate?
Agentic engineering playbook across teams
How the CTO Office connects
Every product, channel and AI agent the company runs passes through engineering at some point. The agents that serve the CTO Office share the same platform and controls as the offices around it, so what’s built fast is also built to run.
How we’d start
Pilot 1
One agentic squad, one quarter
↳
Delivery agents and a principal engineer embedded in one willing team
↳
A baseline on the DORA metrics before the start
↳
Lead time, failure rate and developer experience measured every week
Your decision
Whether to roll the playbook out to every team.
Pilot 2
Test generation on one legacy service
↳
Code analysis agents map one fragile service
↳
Tests generated to lock in current behaviour
↳
Coverage, incidents and change confidence compared before and after
Your decision
Which services to modernise next, and how.
Both run under our Validation First Framework. They’re tested on your own data before you commit a larger budget, and if the numbers don’t hold, we tell you.
Your intervention map
Bring your delivery metrics, your architecture and the service everyone is afraid to touch. We bring the CTO Office canvas. In one session we map it to your teams and pipelines, and mark where agents would pay back first.
01
Your CTO Office canvas
What you run and answer for, checked against how your engineering teams really work.
02
Your value chain
The canvas placed on your industry and your product, with the platforms behind every release.
03
Your intervention points
Each leak with an agent design, its controls and the KPI it moves. Delivered as a mind map, an architecture view or an interactive dashboard.
AI for the CTO Office, answered
What can AI do for the CTO Office?
Agentic AI speeds up software delivery without lowering the bar. Agents work beside engineers to draft requirements, write and review code, generate tests, analyse legacy code, keep dependencies patched and handle routine incidents. Lead times fall, quality holds and engineers spend more time on design and hard problems.
How does agentic AI change software delivery?
Most delay sits between steps, not in the coding. Agentic delivery puts requirement, coding, test and review agents beside each team, so work moves from idea to tested change with fewer handoffs. Engineers stay in charge of design, approval and anything that touches production.
Can AI write production quality code safely?
With the right guardrails, yes. Every agent change goes through the same review, tests and security checks as human code, and engineers approve what is merged. Clear standards for prompts, context and review keep quality consistent across teams.
How should we measure the impact of AI coding tools?
With delivery outcomes, not lines of code. Track the DORA metrics, deployment frequency, lead time for changes, change failure rate and time to restore, alongside escaped defects and the share of work done with AI. If lead time drops and failure rate holds, it’s working.
Can AI help reduce technical debt and modernise legacy code?
Yes. Code analysis agents map legacy code, dependencies and dead code, then generate tests that lock in current behaviour. That makes it safe to refactor or replace in small steps, instead of planning a risky big bang rewrite.
Where should a CTO start with agentic AI?
With one team for one quarter. Embed agents and a principal engineer with a willing squad, set a baseline on the DORA metrics and measure the change. Test generation on one legacy service is a strong second pilot.
