Introducing SHIP: the agentic engineering platform you need

I have spent twenty years working across the software development lifecycle (SDLC). I worked in specialist roles on enterprise systems, being a generalist by heart since the start. At enterprise scale, a decision made in one part of the system affects the rest of the development stages. Architecture affects delivery, delivery affects cost, verification affects the cycle time. The way business and engineering work together affects all of it.

One of the most complex projects I worked on was an internal no-code platform for building high-load, omnichannel applications across multiple platforms. That kind of work teaches you to respect foundations. Projects change and people move on, but the software still depends on the engineering practices underneath it. Skip those practices and the consequences arrive later, usually when the system is under pressure.

Once agentic coding became de facto way of working for the tech industry, many practitioners started to drift away from engineering principles with the excitement of accelerated build capacity. Agentic SDLC is actually a traditional SDLC under pressure. AI being an amplifier for your systems and way of working, makes you realize in no-time that you need a sharp system to keep the quality and speed of software deliveries on par.

These lessons and the problems below shaped the idea of SHIP.

AI adoption got ahead of agentic engineering

Agentic engineering is still very young. Many organizations adopted coding agents before they invested in the structure, training, and systems needed to use them well. They added a new tool to an existing development process and expected the tool to transform the outcome.

Instead, teams often get inconsistent outcome, bottlenecks restricting the quality and the speed of the delivery, rising AI bills, and little evidence that software is reaching production faster.

Furthermore, the industry has adopted a strange status metric: tokenmaxxing. It's a silly valley trend that rewards rapidly burning through AI model tokens. Engineering teams have started to keep internal leaderboards to gamify AI adoption, for a metric that tells nothing about engineering. A large token count proves that someone consumed a large number of tokens. It does not tell you which task they completed, whether the result passed review, how much rework the agent created, or whether a different setup could have delivered the same outcome for less.

Token volume belongs on a cost report. It does not belong on an engineering scorecard.

Measure the work not the appetite

Software engineering already has better measures, like DORA metrics. Cycle time, lead time for changes, deployment frequency, quality, and cost tell us whether a delivery system is improving.

Agentic delivery gives us a chance to measure those outcomes with unusual precision. A mission can precisely attribute time and cost to each stage of a unit of work for the first time in history. A CI failure can return the development back to the builder when it occurs. A review or test comment can become the next action without waiting for another meeting or handoff.

This is where AI becomes an engineering system rather than a collection of individual tools. Teams can change one part of the setup, compare the result, and learn which combination of agents, models, instructions, and quality gates works best for their own codebase.

Why I built SHIP

As I decided to follow my own ventures at the beginning of this year, and walking solo - I needed to ship software faster and at a lower cost with AI without compromising the quality. Most agent products focus on one stage of the lifecycle, usually coding or review. The result still leaves an engineer coordinating the work between tools and babysit the progress of the SDLC.

SHIP runs the delivery loop around an agent team. An assigned issue moves through planning, building, review, deployment, and testing. Review and test agents work as quickly as the builder, and each gate provide specific feedback into the next cycle. Engineers remain in control of approvals and exceptions without having to push every routine handoff forward, or without being locked into a closed agentic system.

The loop matters because generated code should face the same standards as any other change engineers typically deliver. A fast first draft or prototype has limited value if it creates more work in review or fails in production.

It's also a game changer for the economics of model choice running in a loop.

A strong loop changes which model you need

The builder is the most time-consuming stage of agentic delivery, so it has a large effect on cost. Teams often put a frontier model in that seat by default. I wanted to know whether the surrounding system could make a less expensive model reliable enough for everyday product work.

I tested eleven 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 reliable builder was an open-weight model at $3.09 per resolved task. Claude Haiku resolved most of the tickets at $3.43 on average, compared with $6.40 for Opus.

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 model available.

An extensive benchmark study with more proof is in works and will be published soon.

SHIP is built through SHIP

About 90% of this platform has been shipped by SHIP, and that share is growing. Dozens of verified pull requests, every release, and every production deployment have benefited from the same loop the product offers to customers.

Dogfooding removes a lot of comfortable assumptions. Weak and wasteful agent configurations become visible and show up in the bill. A missing quality gate eventually produces a real defect.

This is a healthy pressure. SHIP should earn its claims on its own repositories before asking another engineering team to trust them.

The kind of company I want to build

Most AI products make more money when their customers consume more tokens. That creates an uncomfortable incentive for a platform that also claims it will help customers work efficiently.

SHIP does not add a margin to your inference. Missions run on the models, tools, and provider contracts your own organization chooses and adopts. Teams should be able to change a model or agent harness without rebuilding their delivery process around a vendor. AI sovereignty is practical; it gives engineering teams the freedom to experiment and make decisions based on their own results.

I also do not see agentic engineering as a project to remove humans from software development. Strong teams use AI to move faster, not to replace thinking 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.

In five years, I expect well-run engineering teams to spend much less time on repetitive development work and much more time improving the systems around their agents. I want SHIP to help them do that honestly, with a clear account of the value created, the cost incurred, and the control that remains with the customer.