Short answer: almost every development team now uses AI coding agents, so the question for a buyer is no longer "does my vendor use AI?" but "how do they check what the AI writes?" Ask any agency or freelancer five things before you sign: who reviews every change, what automated security checks run on each pull request, how they stop invented or risky dependencies, who owns the code and the AI costs, and how they measure finished, working features rather than lines of code. If the answers are vague, the speed you are paying for will come back later as bugs, security holes and rework.
This guide is for founders, product owners and agencies in the US, UK and Pakistan who are commissioning a web app, SaaS product or internal tool in 2026 and want the benefits of AI-assisted development without inheriting its risks.
Why this matters right now
AI agents have moved from autocomplete to doing whole tasks. In GitHub's 2026 Octoverse report, presented at GitHub Universe at the end of October, 40% of pull requests on the platform in September started with an agent, up from 17% six months earlier, as The Deep View summarised. The same report shows AI-assisted code review and GitHub Advanced Security both growing more than 90% year over year: teams are generating more code and, sensibly, spending more effort checking it.
The checking is not optional. Veracode's testing of 18 AI models, published on 29 October 2026 and reported by DevPro Journal, found an average security pass rate of 62%, meaning roughly 38% of coding tasks produced vulnerable code. Speed and safety are separate properties, and only the second one is your vendor's job to guarantee.
There is also a quality signal hiding in the productivity numbers. Data from developer analytics firm DX, discussed on the Atlassian blog, shows AI adoption above 95% across more than 500 organisations and pull request throughput roughly doubling, yet pull requests getting much larger and engineers reporting lower confidence in the changes they ship. More code is not the same as more working product.
The five questions to ask any development partner
1. Who reviews every change, and how?
An AI agent can draft a feature in minutes, but someone with context has to read it, run it and decide whether it belongs in your product. Ask whether every pull request gets a human review by a senior engineer, whether reviews check behaviour against the original requirement (not just style), and whether large changes are split into reviewable pieces. A team that merges agent output with a thumbs-up is shipping a first draft.
2. What automated checks run before anything is merged?
Good teams put a gate in the pipeline that AI code cannot skip. At a minimum, expect:
- automated tests that run on every pull request, with new features shipping with new tests;
- static analysis (SAST) that flags injection, auth and secrets problems;
- dependency scanning (SCA) for known vulnerabilities in open-source packages;
- secret scanning, so API keys never land in the repository;
- type checks and a production build that must pass before deploy.
Ask to see a recent pull request with these checks on it. It takes a vendor two minutes to show you, and it tells you more than any sales deck.
3. How do they stop risky or invented dependencies?
AI tools pull open-source packages freely, and they sometimes suggest packages that don't exist, which attackers can then register under that name. Ask how new dependencies are approved, whether versions are pinned with a lockfile, and who keeps them patched after launch. The software supply chain is now a common attack route, as Infosecurity Magazine notes, so this belongs in your contract, not just your hopes.
4. Who owns the code, the data and the AI bill?
Agentic coding uses far more model calls than a chat prompt, and tools have been moving from flat seats to metered usage. MIT Technology Review reports that GitHub Copilot moved to metered credits and that some large companies have burned through annual AI budgets early. Make sure your quote says whether AI tool costs are included or passed through, that you own the source code and repository from day one, and that your private data, customer records and credentials are not pasted into tools that train on them.
5. How do they measure progress?
Lines of code and tokens used are vanity numbers. The useful measures are the ones you can see: features accepted against agreed criteria, defects found after release, and how much work had to be redone. The same MIT Technology Review piece argues for tracking cost per merged change alongside rework and defects, which is a sensible lens for a buyer too. Ask for a weekly demo of working software on a staging link, not a progress percentage.
Red flags in a proposal
- A fixed price and timeline that look dramatically lower than other quotes, with "AI" as the only explanation.
- No mention of testing, code review or security in the scope.
- The vendor keeps the repository, hosting or domain in their own account.
- No staging environment, so you only see the product at the end.
- No plan for maintenance, updates or security patches after launch.
If you already have an app that was built quickly with AI tools and it's starting to wobble, our guide on taking a vibe-coded app to production walks through the clean-up, and AI code-fixing agents covers where automated repair helps and where it doesn't.
What good AI-assisted delivery looks like
Used well, AI agents genuinely make projects faster and cheaper: scaffolding, tests, documentation, migrations and repetitive refactors are exactly where they shine. The DX data above points to faster onboarding and better documentation as clear wins. The trick is to put that saved time into the parts machines are weak at: understanding your business, designing the data model, reviewing security-sensitive code and testing real user journeys.
A healthy process usually looks like this:
- Clear specs first. Each feature has written acceptance criteria before any agent touches it.
- Small, reviewed changes. Agents draft, senior engineers review, and big changes are split.
- Automated gates. Tests, SAST, dependency and secret scanning run on every pull request.
- Staging demos. You click through working features every week.
- Ownership and handover. Code, infrastructure and documentation sit in accounts you control.
How we work at Localhost Labs
We are a software studio in Multan, Pakistan, building web apps, SaaS products and automation for local, US, UK and agency clients. Our engineers use AI coding tools every day, and every change still goes through senior review, automated checks and a staging demo before it reaches your users. You own the repository and infrastructure from the first commit. Typical projects fall in the USD 3k–15k+ range depending on scope; see our SaaS development cost guide for how that breaks down, or our notes on AI agent security if your product itself uses agents.
Want a second opinion on a proposal, or a team that uses AI without cutting corners? Get in touch and we'll review your scope with you.