Live Webinar

Fri · 18 Sep · 14:30 UK

From AI Copilot to Agentic Engineering

Small teams are hitting 10–50× velocity while most engineering orgs plateau at 1.3×. The gap is structural, not tooling. In 25 minutes you learn exactly where it comes from and how to close it.

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Next session: Fri 18 Sep
Time: 14:30 UK
Length: 25 min
Price: Free

What we cover

Act 01

Where we are now

The shift, and why the gap is structural

The pace of progress

Six inflection points in 12 months that redefined what developers do all day, from Cursor and vibe coding to full agentic delivery.

If you missed any of these shifts, you are already behind. We walk the timeline: Cursor (mid 2023), Claude Code (March 2025), vibe coding coined by Karpathy (February 2025), context engineering popularized by Lütke and Karpathy (June 2025), OpenClaw (November 2025), and Karpathy declaring the shift to agentic engineering (February 2026). You leave knowing exactly where the industry is and what your teams should be preparing for now.

The paradox

Small teams hit 10–50× velocity with 3 engineers. Most enterprise orgs plateau at 1.3–1.5× with 16–20. Same tools, different outcomes.

We break the structural differences down across eight dimensions: who writes the code (human vs agent), share of AI-written code (30–50% vs 100%), team size for the same output, velocity vs baseline, human code review (>80% vs 0%), pipeline scope (coding only vs spec-to-deploy), the real bottleneck (coding vs specifying and verifying), and how knowledge and decisions are held. You leave understanding why adding AI tools without changing how you work changes so little.

Act 02

The framework

How to read your org, and what blocks it

Agentic engineering maturity model

90% of companies are stuck at Level 2, smart autocomplete. We show the six levels (L0–L5), the critical threshold, and real case-study numbers.

The framework to assess where your organization sits today and what it takes to move up. For each level we show the human role, share of AI-written code, velocity multiplier, and review rate. The critical threshold is L2→L3: the human stops executing and starts validating. We share Odevo numbers, 16 engineers down to 5, >70% faster, 25 epics in 3 weeks during PoV, AI-curious to AI-integrated in 8 weeks.

The eight blockers

The barriers keeping orgs stuck: Copilot Ceiling, Mindset Shift, Org Tax, Security Wall, Infra, Measurement, Cost J-Curve, Macro Headwinds.

Most AI adoption stalls on organizational friction, not technology. We cover each blocker: the Copilot Ceiling (90% stop here), the Mindset Shift (coder-to-architect identity crisis), the Org Tax (60–80% coordination, 2 hours to build vs 3 weeks to approve), the Security and Procurement Wall (CISO as critical path), Infra you cannot buy, the Measurement Gap, the Cost J-Curve ($19 to $500/dev/month), and Macro Headwinds. Knowing these in advance lets you plan around them instead of hitting them blind.

Act 03

The playbook

The four moves, where to start, what to build

Four moves to agentic engineering

Prove it, codify it, scale it, evolve it, a practical framework for getting from where you are to where the industry is going.

The playbook. Prove it: 1–3 proof teams on real delivery; measure cycle time, throughput, defect rate. Proof creates pull, mandate creates resistance. Codify it: spec formats, handover templates, rulebooks, if it is not written down it does not exist for agents. Scale it: shared toolchain, skill registry, paved roads, security guardrails co-designed with CISO and DevOps. Evolve it: a new skill profile from writing code to specifying intent, new career tracks, champions leading the change.

Where to start

The highest-impact, lowest-risk wins to build momentum, and the traps that stall most organizations.

Start with code quality (test generation, review augmentation, bug triage), knowledge capture (requirements extraction, architecture docs, spec standardization), and focal acceleration (boilerplates, data migrations, onboarding). Greenfield and rebuild projects are the ideal training ground. What to avoid: mature refactoring, security-critical code, production deployment, and premature scaling, building the platform before proof teams, mandating before proving, measuring adoption not outcomes.

The agentic engineering platform

The new capability layer between your product teams and your existing infrastructure, the part you cannot buy.

The future product team is a half-pizza team: 2–3 agentic engineers plus fractional PO, architect, and product designer, owning features end to end while agents do the production work and humans set direction, review, and decide. Underneath sits the new agentic engineering platform (orchestration, guardrails, governance, eval pipelines, paved roads), then your existing DevOps/SRE platform, then cloud infra. We show how to structure and staff the layer.

Who it's for

CTOs & VPs of Engineering

Making decisions about AI adoption and team transformation.

Engineering Directors

Responsible for delivery velocity and team productivity.

Founders & Technical Co-founders

Building products and teams in the AI era.

Heads of Product

Rethinking how products get built and shipped.

C-Suite & Non-Technical Leaders

Accountable for the decisions, wanting a clearer view before making them.

Private Equity & Operating Partners

Driving AI-native transformation across portfolio companies to move velocity and margin.

Presented by

What to expect

Practitioner-led

Not analysts reading slides. Run by people who build AI-native products and transform organizations every day.

Grounded in the field

Drawn from transformations inside large, complex organizations, the org friction, security walls, and cost curves that actually slow teams down, not toy demos.

A clear map

A maturity model to place your org on, the blockers to plan around, and the four moves that work. Enough structure to make your next decision with confidence.