About SHIP

Agentic engineering needs engineering metrics.

I built SHIP to help teams deliver software faster and at a lower cost with AI, without lowering the quality bar or giving up control of how the work gets done.

Önder Ceylan speaking on stage while holding an award — Önder Ceylan, founder of SHIP and award-winning AI engineering leader

Why I built it

The system around the agent matters.

I have spent twenty years working across the software development lifecycle. I worked in specialist roles on enterprise systems, being a generalist since the beginning. I understand how one stage in lifecycle affects all the rest.

One of the most complex projects I worked on was an internal no-code application platform built for high load, multi-platform delivery, and omnichannel experiences. That type of work teaches you to respect foundations and principles.

Projects and people come and go. The work still rests on foundational engineering practices. If you skip them, the consequences arrive later, usually when the system is under pressure. SHIP carries those practices into agentic engineering through explicit quality gates and a direct view of cost.

A better measure

Tokenmaxxing is a spending story, not an engineering metric.

A token count says how much budget someone burned. It says nothing about the task, the outcome, the agent configuration, or how much repetitive and out-of-context work happened along the way.

Software engineering already has useful measures: cycle time, lead time for changes, deployment frequency, quality, and cost. An agentic delivery loop can measure many of them for each unit of work with unusual precision. A CI failure can be fixed at the moment it occurs. Review or test feedback can be addressed as they arrive.

Teams should experiment with every stage of their delivery system and see whether an alternative agent or model actually holds the quality bar, and moves metrics that affect the speed and cost of the delivery.

Evidence from the SWE-in-a-team benchmark

A strong loop changes which model you need.

Building a spec is the most time-consuming stage of agentic delivery, so it carries most of the cost. This is not a new problem. None of the teams I worked with had every member at principal or staff level. Less experienced people get enabled and corrected by their more experienced peers, and agents work the same way.

I tested thirteen builder configurations on 20 real-world SaaS tickets on a full-stack app while keeping a frontier planner, reviewer and QA agent fixed. Seven builders resolved all 20 tickets. The cheapest configuration to resolve all 20 tickets was DeepSeek-v4-flash on pi, at $3.34 per resolved ticket. Claude Haiku resolved 18 of 20 tickets at $4.23 per resolved ticket, compared with Claude Opus, which resolved all 20 at $5.48 per resolved ticket.

This was a small directional pilot with one repository, 20 tickets, and one primary trial per setup. It does not prove that a budget model can handle every job. However, it does show why teams should test model choices inside a complete delivery loop instead of assuming every role needs the most capable and most expensive model available.

Read the complete SWE-in-a-team benchmark research here.

Built in practice

SHIP ships SHIP.

The platform has opened hundreds of verified pull requests across SHIP's codebases, and partner repositories - each one planned, built, reviewed, and tested by the same delivery loop the product offers to customers.

Dogfooding removes a lot of comfortable assumptions. Weak and wasteful agent configurations became visible and showed up in the mission reports. A missing quality gate eventually produced a real defect and slowed the delivery pace.

This was a healthy pressure. SHIP has earned its claims on its own repositories before asking another engineering team to trust them.

Our pillars

Four principles I want SHIP to keep.

Engineering quality is non-negotiable.

Generated code goes through the same gates: review, test, CI, and acceptance as any other change. Faster output is useful only when the result matches the standards and quality expectations.

Measure outcomes, not consumption.

Token volume is a session stat and a cost input. Cycle time, lead time, delivery frequency, quality, and resolved work tell an engineering team whether the system is healthy and improving.

Humans and agents work together.

Agents take on repetitive delivery work. Engineers set direction with their understanding and judgment, handle exceptions, and improve the practices that make the next mission run better.

Customers keep control.

Teams should be able to switch agents, models, and providers without rebuilding their workflows. The platform should help reduce AI costs, not profit from making them larger.

Founder

Önder Ceylan

Founder of SHIP and award-winning AI engineering leader

Önder has worked across software engineering, developer platforms, technical leadership, open source, and developer training for the past 2 decades. His work focuses on the systems that help people and agents build reliable software together.

Selected recognition

Google Developer Expert in Web Technologies and AI for Web Developers
Golden Award in B2B, KLM
Chrome Advanced Web Apps Fund, Google Chrome
JavaScript Hero of Community, JS Poland

The direction

In a few years, engineering teams will spend less time doing repetitive development work and more time improving the agentic systems around it.

Strong teams use AI to move faster, not to replace understanding, judgment and creativity. The best outcomes come from pairing talent (people) with the right tools (agents). People and agents together build better systems. Agents can take on repetitive delivery tasks while engineers can focus on architecture, exceptions, integrations and the engineering system that makes the next mission better.

Ready to put the fleet to work?

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