Agentic development is not a chatbot bolted onto your product. It is a change to how the software gets built: senior engineers directing autonomous agents that plan, write, test and review in parallel, with a named human accountable for everything that reaches your main branch.
What the pipeline is made of, and where the human judgment sits inside it.
Work decomposed so several agents make progress at once — API, interface, tests, migrations, documentation — while engineers hold architecture, sequencing and the judgment calls that agents are measurably bad at.
Every commit meets automated regression, generated edge cases and integration runs, including the cases a tired human would not think to write. Human QA still signs off anything that ships.
No agent output reaches your main branch unreviewed. The speed comes from work happening in parallel, not from skipping review — which is precisely the corner most AI-assisted delivery quietly cuts.
Anthropic Claude, OpenAI and retrieval over your own code and documents, chosen per task. Nothing in the delivered software depends on which model helped write it, so there is no lock-in to inherit.
Named up front, because avoiding them is most of the value.
Generated code that passes its tests can still encode an assumption no one intended. Review is the only real control, and it is the first thing dropped once a team is impressed by the volume of output.
An agent can produce something demo-ready in an afternoon, which makes it tempting to skip the error handling, migrations and access control that turn a demo into software you can operate.
Agents fail on codebases they cannot see properly. Time spent making a repository legible — structure, tests, documented conventions — buys more than any amount of prompt tuning.
Putting AI in the pipeline blurs accountability unless one person is explicitly responsible for each release. On our engagements that is a named senior engineer, every time.
Every phase is run by senior engineers and ends in something you can see.
We look at your codebase and workflow and say where agents genuinely help and where they do not. Some work is a poor fit, and it is cheaper to hear that before the estimate than after it.
Conventions documented, thin test coverage raised, environments made reproducible — the unglamorous work that decides whether agents accelerate you or generate liabilities.
Agent roles, task decomposition, guardrails and review gates no change can bypass, configured around your branch strategy rather than ours.
Several work streams progressing at once against an agreed architecture, with engineers steering and resolving whatever the agents escalate.
Automated suites plus human QA on every path that touches money, personal data or credentials. A passing test is evidence, not a guarantee.
Either your team picks up a documented pipeline or we keep running it. Both leave you owning the repositories, the prompts and the tooling configuration.
Chosen per project, and for what your team can maintain afterwards.
Only with your agreement, and on terms you have seen in writing first. Which provider is used, whether prompts and code may be retained, and which repositories are in scope are all settled before work starts. Where your policy or a client contract requires it, the work runs against models that do not train on submitted content.
An assistant helps one engineer type faster. Agentic delivery restructures the work so several agents run in parallel under one engineer’s direction, with task decomposition, test generation and review gates built into the pipeline rather than left to individual habit.
No. It reflects the work agents suit — well-specified features on a codebase they can read. Ambiguous requirements, undocumented legacy systems and heavy stakeholder negotiation do not compress, and we say so during the fit assessment rather than after you have signed something.
We are. A named senior engineer reviews and signs off every release, and accountability sits with us exactly as it would on a conventionally built project. That is the entire point of keeping humans in the pipeline.
Yes. It works best where conventions are documented and tests already exist; where they do not, the groundwork phase covers it. Your branch strategy, CI and review rules stay in force — the pipeline adapts to them, not the other way round.
No. What we deliver is ordinary code in mainstream frameworks that runs with no agent involved anywhere. The pipeline configuration and prompts are handed over with the repository, so your team can carry on using them or drop them entirely.
Sales Cloud, Service Cloud and marketing automation configured around how your business really sells — with adoption as the deliverable.
Solution, visual and interaction design handed over as a system engineers can build from — accessibility designed in, not audited later.
Connectivity, telemetry, device management and dashboards for connected products — with the update path designed in from the start.