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 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 platform for building high-load, omnichannel applications across multiple platforms. That type of work teaches you to respect foundations and principles. Projects change and people move on, but the software still depends on the engineering practices underneath it. If you skip those practices, 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 peers 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 a short 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 - aka slop, bottlenecks restricting the quality and the speed of the delivery, rising AI bills - for the cost of software 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 cost or time spend.

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 healthy and 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 immediately 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 2026, 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 stay in control of approvals and exceptions without having to coordinate every routine handoff forward, or without being locked into a closed agentic system like today's vibe coding tools.

The loop matters because generated code should face the same standards, and go through the same gates 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 when models run in a loop.

A strong loop changes which model you need

Building 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. And, this is not a new concept. None of the teams I worked with had all the members at principal / staff level. Less experienced team members get enabled and corrected by their more experienced peers. People and agents work the same way as agents mimic human behavior.

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 expensive model available.

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

SHIP is built through 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 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 down 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.

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 claims it will help customers work efficiently, and save cost.

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 practice to remove humans from software development. 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.

In the following 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.