Agentic Software Development Services
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.
Agentic Software Development done properly.
What the pipeline is made of, and where the human judgment sits inside it.
- Autonomous Build Agents
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.
- QA Agent Pipeline
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.
- Senior Engineer Review
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.
- Built on Modern LLMs
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.
Where this usually goes wrong.
Named up front, because avoiding them is most of the value.
- Code nobody has actually read
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.
- Prototypes mistaken for products
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.
- Context is the constraint, not cleverness
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.
- Nobody named on the release
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.
The engagement, phase by phase.
Every phase is run by senior engineers and ends in something you can see.
Fit assessment
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.
Repository groundwork
Conventions documented, thin test coverage raised, environments made reproducible — the unglamorous work that decides whether agents accelerate you or generate liabilities.
Pipeline setup
Agent roles, task decomposition, guardrails and review gates no change can bypass, configured around your branch strategy rather than ours.
Parallel delivery
Several work streams progressing at once against an agreed architecture, with engineers steering and resolving whatever the agents escalate.
Verification
Automated suites plus human QA on every path that touches money, personal data or credentials. A passing test is evidence, not a guarantee.
Handover or continuation
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.
What we build it with.
Chosen per project, and for what your team can maintain afterwards.
Questions we get asked.
Is our code sent to a third-party model?
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.
How is this different from developers using an AI assistant?
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.
Does the 3× figure apply to every project?
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.
Who is responsible if agent-written code causes a problem?
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.
Can you work this way inside our existing team and repositories?
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.
Will we be locked into your tooling?
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.
Further reading.
What we have written about agentic software development and the decisions around it.
- Using AI agents →
A practical look at where AI agents genuinely help in product development today, where they still need human oversight, and how to decide when to use them.
- A guide to low-code development →
How low-code platforms accelerate delivery without sacrificing quality — where they genuinely fit in a modern software stack, and where custom code wins.
- Finding the Right Development Partner →
How to evaluate a software development company before you sign: the questions to ask, the red flags to walk away from, and what a good pitch looks like.
Other services.
- Salesforce Implementation →
Sales Cloud, Service Cloud and marketing automation configured around how your business really sells — with adoption as the deliverable.
- Software & UI/UX Design →
Solution, visual and interaction design handed over as a system engineers can build from — accessibility designed in, not audited later.
- IoT Software Development →
Connectivity, telemetry, device management and dashboards for connected products — with the update path designed in from the start.