AI agents across the whole lifecycle
Devstorm uses a custom agentic software tooking to build agents into every step of the software development lifecycle, from research and planning to implementation, validation, and review. Experienced engineers steer at the checkpoints that matter, so a weak assumption in research does not turn into 100,000 lines of code nobody can maintain. The agents move fast. The people keep them building the right thing, on solid foundations.
Discover what is actually known. Let evidence resolve questions. Refine the work without inventing missing intent.
An engineer who misunderstands a task loses half an hour before someone notices. An agent that misunderstands the same task can change dozens of files, write tests that agree with it, update the documentation, and spend a real budget doing all of that.
The answer is not to approve every step, which only turns people into the bottleneck. Human judgement sits at the points where a mistake would compound. Around those points, agents take on the work that used to eat the time: finding and cross-checking scattered guidance, closing research gaps, working through technical spikes, turning rough ideas into prototypes people can react to, and producing the structured output that follows every decision.
We confirm the direction before agents start running. A short conversation up front is cheaper than a confident agent heading the wrong way.
If the answer is already in the code, the tests, or the project's records, agents use it. You are not asked questions that have already been answered.
You decide intent, trade-offs, and risk boundaries. You also decide where agents must stop for guidance before an irreversible step. Agents do the investigation around those decisions.
A green build proves that something ran. It does not prove the change does what was asked. Tests are based on acceptance criteria that people set, and every result is verified against them.
Code can be correct in isolation and still be a poor fit for the codebase around it. Agent-written code follows the patterns already in place, and stays readable to your engineers.
Agents make the change the acceptance criteria need. They do not refactor, rename, or tidy code beyond the task. When they see a possible improvement, they report it separately for you to decide on.
Bring people in at the judgement calls, not just because a process stage has ended.
Not every change needs every stage. A one-line fix does not need a discovery phase. The amount of process matches the size and uncertainty of the work.
The toolkit
Every skill is hand-built and refined through iteration, evaluation, and real delivery. Each one has a defined responsibility, firm boundaries, and an explicit stopping point. A request to investigate a bug does not quietly turn into a fix in production code.
Describe the job, the source material, and your limits. The lead agent checks the requirements and your project rules, states its strategy, and gets on with it.
It might keep tightly linked work to itself, hand independent pieces to subagents, or move through research, planning, and implementation in phases. It changes approach when the evidence says so, and it stops at the boundary you set. It asks you about intent and authority, not about routine choices.
The toolkit also writes for people. Pull requests, work items, and documentation follow a technical-writing standard based on Simplified Technical English, with a final pass that removes vague and inflated wording.
Extensions to the coding agent harness encode engineering conventions and guardrails. Your project rules still decide what happens to branches, pull requests, merges, and deployments.
The lead agent keeps its own context lean, because every turn resends it. Long, noisy, or independent work goes to subagents. Small, tightly linked work stays put.
Traces record what the agents did and why, so we can evaluate their behaviour against evidence rather than impressions.
Agents run in containers that hold only the workspace, tools, and credentials the work needs. They have room to work, and nothing else to reach. More on that below.
Every expensive agent action should reduce uncertainty, make implementation progress, or produce validation evidence. If it does none of those, the agent should stop and think again.
Getting started
Most codebases were not built with agents in mind. The first thing the toolkit does is build the grounding knowledge agents need to work in yours. It works from evidence, and asks you only about what the evidence cannot settle.
It reads the repository, source control, work tracker, documentation, and any existing decision records. Facts the project can supply are its job, not yours.
You confirm which repositories, projects, and teams it may work in. Having access to something does not put it in scope.
It shows you what it found as interpretations to confirm or correct. There is no setup questionnaire. Questions come only when the answer changes something.
It records what it learned in a project profile that your team can read and edit, with or without the toolkit.
It never writes credentials into the repository, and it never rewrites your existing agent instruction files. Run it again when things change. It reconciles the differences and keeps your edits.
Contained autonomy
An agent that has to ask before it reads a file or runs a test is not much use. You become the bottleneck again. An agent with the run of a developer's machine can reach every repository, token, and cloud session on it.
Telling an agent what not to touch is a soft boundary. Given a task and a way around a restriction, agents sometimes take it. So our agents work in containers. Each one gets a full development environment, but it sees only what we put inside: the project workspace, the tools the work needs, and credentials scoped to what it is allowed to do. It cannot reach production, because production is not in its world.
Credentials are injected deliberately, and fine-grained tokens grant only what the work needs. An agent might be able to view the main branch but not commit to it, inspect the release pipeline but not trigger a deployment, or know that a key vault exists without being able to read its contents.
Hooks stop dangerous actions before they run, such as commits to protected branches, destructive commands, or edits to secrets. The container limits what an agent can reach. Hooks limit what it can do there.
Each agent that writes in parallel gets its own worktree or clone and its own branch, so every file has one writer and agents cannot overwrite each other's changes. Agents that only need to read get read-only access.
Prompting a boundary helps. Enforcing it helps more.
Every agent session starts cold. Without a record, you are onboarding a brilliant new senior engineer every morning, and each one rediscovers the same facts or guesses why the system is shaped the way it is. The toolkit records what is established as fact, which decisions were made, and why. Anything surprising, or likely to come up again, is written down with its reasoning, where both people and agents can find it.
Code shows what was built, not why. Consequential decisions are recorded with the alternatives considered and the reasons they were rejected.
The domain model and its terms live in one place, so people and agents use the same words for the same things.
A finding becomes permanent only when future work benefits from it. Task notes and handoffs are short-lived, so they do not pile up as clutter.
Knowledge lives in your repository, unless you already have an established home for it. Project-specific agent instructions go through your normal review and approval, like any other code.
Persist what cannot be rediscovered. Rediscover what is likely to have changed.
The toolkit does not impose a tracker, a work-item hierarchy, a branching model, a language, or an agent harness. It reads your conventions from the repository first, and asks you only about what it cannot find.
Greenfield and brownfield, from an early idea to a large, complex system, in whatever language it is written in.
GitHub, Azure DevOps, GitLab, Jira, Linear, or plain Markdown files. Whatever you track work in, the toolkit adapts to it.
Claude Code, GitHub Copilot CLI, Codex, Pi, OpenCode, or something else. The skills are portable, so they go wherever your agents already run.
The cost of reaching a decision drops. Authority over the decision stays with the person accountable for it.
PRODUCT
You need a product, and you need it soon. Tell us the outcome you want, and we deliver it end to end with our agentic workflow. You stay involved at the decisions that matter.
DELIVERY TEAM
You need engineers inside the business, working on your project. We come in as a team and work alongside your people and your processes. Agents give a small team the capacity of a much larger one, so you get faster turnaround, more consistent quality, and lower cost.
ENABLEMENT
You want your own engineers working with agents. We train them, set up contained agent environments, guardrails, and workflows, and embed the toolkit or tooling of your own for future use. The capability stays after we leave.
Devstorm was founded by Dave Toland, who has spent more than 25 years in software engineering and technical leadership across financial services, pensions, trading platforms, public safety, and data governance. Most of that work came before AI coding tools existed. That is where the toolkit's habits come from. Dave on LinkedIn
A product built for you, a team inside your business, or your own engineers working this way. Start with an email.