Your team, with the tools that run our own development.
Bought AI licenses and watched your developers drift back to the old way? We install on your repo what we work with every day: the toolkit, the Skills and the development rule book. Then we work with the team on your own code until it becomes how they work.
An AI license is not a way of working.
Agentic Engineering is a way of working in which AI agents write and test code, while developers define the task, supervise and own the result. Most teams do not get stuck because of the tool. They get stuck because three things were never defined together:
- What the agent receives before it starts: which spec, in what form.
- Which rules it must respect, even when nobody mentioned them in the task.
- Who checks what, and when an agent’s work counts as done.
Without that, every developer works differently and the pilot stays a pilot.
Not a slide deck. What we work with every day.
Consultancies sell recommendations. We are a software house, and we hand your team the same method and tools we use to build for clients and to build our own products.
A spec that leads every step
Spec-Driven Development, where the spec leads every step: each requirement becomes a spec in two versions, one for stakeholders and one for developers and agents, and from there becomes tasks.
The development rule book
Hundreds of rules gathered over decades, in chapters: UI/UX, security, performance and more. They go into the agents’ instructions, and we align them with the standards you already have.
The toolkit and the Skills
The tools and Skills we built for working with AI agents, installed on your repo and connected to your existing environment.
Hands-on work on your code
Not generic training. We work with the team on real tasks in your repo until the method runs without us.
We are going through this change ourselves. Let us do it together.
Right now we are moving our own products, across different technologies, to an environment built on AI agents, with the same tools we will install for you. We do not have years of results to show yet, and we will not invent them. What we do have: tools we work with every day, development experience since 1989, and an AI system that tests DocsRobot, our own product, every night.
- A small number of companies, so we can work closely with each team.
- Early partner terms.
- We learn together: what we learn with you goes into the method, and what we learn in-house reaches you.
Start with one team. Expand when it works.
Call
An hour with whoever leads development. How the team works today, on which technologies, and what you have already tried with AI. At the end: whether it is a fit, and a fixed price for the next step.
Assessment and plan
We review your workflow and one or two repos, and define together: which project to start with, which chapters of the rule book come first, and what you will measure to know it works.
At the end: a rollout plan with time and budget.
Rollout and coaching
We install the tools, the Skills and the rules, and work with the team on real tasks. More teams join only after the method runs in the first one.
Rules the agent does not forget.
An AI agent writes code that passes the first test and breaks under load, under concurrency or against an external provider. A relevant rule that is not obvious goes into the instructions for developers and agents as a “remember that” note. Three examples:
Two users edit the same record. Neither silently overwrites the other.
What you get: data integrity when people work together. Nobody’s work disappears without them knowing.
The rule: every editable record has a version exposed as an ETag header. An update must include If-Match with the version that was read. If the record changed in the meantime: 412, and the user gets a clear message. Never a silent last-write-wins, not even against background processes.
A double click never performs an action twice.
What you get: no double charges and no duplicate records, even when the network is slow and the user clicks again.
The rule: every operation with side effects takes an Idempotency-Key. The server claims the key atomically, stores the response, and returns it on every repeat of the same key. Same key with a different body: 409.
When an external system goes down, yours keeps working.
What you get: when a payment provider, an email service or an AI model does not respond, the system keeps working on everything that does not depend on it. And when something goes wrong, a reference number leads to the full picture within minutes.
The rule: every call to an external service is wrapped in a retry with exponential backoff and jitter, only for errors where a retry can succeed, and in a circuit breaker that opens after repeated failures. Every request gets a correlationId that is attached to every log line and shown to the user on a critical error.
Before we start
Can you guarantee the team will work faster?
Does this replace developers?
We already use AI tools. Do we need to replace them?
What about security?
How long does it take?
One hour. No commitment.
At the end you will know what we understood, which questions are open, and what the spec will cost. If we think it is not worth building, we will say that too.
Or directly: info [at] assembly-infotech.solutions
Plot no. E-309, Phase 8/A, Mohali, Punjab 160055, India