AI Agents for Mobile QA: Lessons for Uganda in 2025

Mobile teams in Uganda lose money in a very boring way: bugs that escape testing. A checkout screen that fails on one Android version. A USSD flow that times out at step 5. A “Send Money” confirmation button that’s tappable on one phone size but half-hidden on another. None of these problems feel dramatic—until customer support lines light up, transaction success rates drop, and trust takes a hit.

That’s why Thunder Code caught my attention. The startup is building AI-powered “agents” that mimic human testers, aiming to speed up QA and catch subtle UI and UX issues that manual testing often misses. The company reportedly raised $9M early, and it’s being led by one of Africa’s most proven founders—an important signal that serious African capital and talent are moving into practical AI, not hype.

This post is part of the “Enkola y’AI Egyetonda Eby’obusuubuzi n’Okukozesa Ensimbi ku Mobile mu Uganda” series, where we focus on what actually improves mobile services and mobile money experiences. Thunder Code’s story matters because it points to a simple reality: if you can automate the slow, repetitive parts of quality assurance, you can ship faster without gambling with user trust—especially in fintech.

Thunder Code’s bet: AI agents that test like humans

Thunder Code’s core idea is straightforward: replace a big chunk of manual, repetitive QA work with AI agents that behave like real users. Instead of only checking whether code compiles or whether a unit test passes, these agents attempt typical tester behaviors—clicking through screens, validating flows, noticing layout issues, and learning from feedback.

Traditional automated testing usually relies on brittle scripts. A small UI change breaks a test even if the app still works. Human QA catches nuance (visual glitches, confusing wording, misaligned elements) but is slow and expensive.

Thunder Code is aiming for a middle path:

• Agents simulate QA processes (like a tester would)
• They catch subtle UI/UX issues that aren’t obvious in logs
• They learn from feedback, improving over time

A useful mental model: scripted automation checks if the “door can open.” AI agents try to walk through the door while carrying groceries, in a hurry, on a shaky network.

For Ugandan teams building mobile money, agent banking tools, savings apps, and merchant payment apps, this style of testing is closer to reality—because your “real world” includes inconsistent connectivity, device fragmentation, and users who won’t forgive confusing screens.

Why QA is a profit problem for Ugandan mobile money and fintech

Most companies get this wrong: they treat QA as a “last step” rather than a revenue protection system.

When quality slips in mobile financial services, the cost shows up in predictable places:

The hidden costs of weak mobile QA

• Failed transactions → immediate revenue loss and reconciliation workload
• Support tickets → higher operational costs and longer resolution times
• Churn → customers stop trying after 1–2 bad experiences
• Fraud and risk exposure → bugs in limits, session handling, or OTP screens create openings
• Partner damage → banks, telcos, and aggregators lose confidence in your releases

In Uganda, where mobile money and digital payments are everyday infrastructure, trust is the product. If a user fears that a transfer might “disappear,” they revert to cash or stick to one provider.

AI-driven QA fits this market because it targets the exact pain point: the slow, manual testing that delays releases and still misses the weird edge cases.

Where AI-powered QA agents can help most (practical use cases)

AI agents aren’t magic, but they’re very good at doing what human testers hate: repeating workflows across many device shapes, OS versions, and network conditions.

1) UI consistency across Android devices

Uganda is an Android-heavy market, and Android fragmentation is real. One layout can behave differently across screen sizes, fonts, and OEM customizations.

An AI testing agent can:

• open the same screen across multiple emulated devices
• attempt taps and swipes like a human
• flag elements that overlap, disappear, or become untappable

This is especially valuable for:

• KYC capture screens (camera permissions, cropping, glare)
• PIN setup and confirmation flows
• error states (low balance, invalid number, daily limit reached)

2) End-to-end flow testing for mobile money journeys

Fintech apps aren’t just “screens.” They’re journeys.

AI agents can repeatedly run scenarios like:

1. register → verify identity → add beneficiary
2. deposit → send money → receive receipt
3. reverse a transaction → confirm → check wallet balance

The goal isn’t to replace your business logic tests. It’s to catch the “human” issues:

• confusing confirmation language
• missing or ambiguous fee disclosures
• “success” screens that don’t match actual wallet updates

3) Testing under bad network conditions (the real Uganda)

If your QA assumes stable 4G and low latency, you’re testing a fantasy.

AI agents can simulate:

• intermittent connectivity
• slow responses
• dropped sessions mid-flow
• retries and timeout handling

The payoff is direct: fewer stuck transactions and fewer panicked customers.

4) USSD + app parity (and why it matters)

Many Ugandan services still rely on USSD alongside apps. The user experience across channels often diverges: fees shown in one place but not another, steps missing, or confirmations unclear.

While AI agents are most naturally aligned with app UI testing, the broader idea—automating flow validation—can be extended to multi-channel QA:

• validate that USSD fees match app fees
• ensure limits and wording are consistent
• check that receipts/notifications align

If your business runs both channels, parity testing is not “nice to have.” It reduces disputes.

A realistic adoption plan for Ugandan product teams

You don’t need to “transform everything.” The teams that win are the ones that pick the right first wedge.

Step 1: Start with the 5 flows that create 80% of complaints

Pick your highest-risk, highest-volume journeys. For many mobile finance products, it’s:

• login + session refresh
• send money
• cash-out/withdraw
• add card/bank (if applicable)
• transaction history + receipts

Instrument these flows with AI-assisted end-to-end tests first. That’s where you’ll see ROI.

Step 2: Pair AI agents with human QA (don’t replace them)

AI agents are fast. Human testers are wise.

A strong model is:

• AI agents run repetitive flows daily (and on every build)
• human QA focuses on exploratory testing, edge cases, and new features
• product managers use agent reports to prioritize UX fixes before release

If you try to fire your testers and “go fully AI,” quality will drop. The tool will become a scapegoat.

Step 3: Treat agent feedback as product data

Thunder Code’s concept of agents that “learn from feedback” is powerful only if you have a feedback loop.

Make it practical:

• tag defects by type (layout, copy, crash, performance, permissions)
• track “escape rate” (bugs found after release)
• measure time-to-fix and time-to-detect

A snippet-worthy rule I’ve found useful:

If you can’t measure time-to-detect, you’re not running QA—you’re running hope.

Step 4: Bake compliance and risk checks into QA

Ugandan fintech teams operate under real compliance constraints (consumer protection, data handling, audit trails). Your QA should validate:

• consent screens and data permission prompts
• masking of sensitive values on screens and notifications
• lockout behavior for repeated PIN failures
• receipt details and dispute-friendly logs

AI agents can help by repeatedly verifying these behaviors across builds, reducing the chance of “we changed a screen and forgot the compliance part.”

What Thunder Code’s $9M signal means for African AI (and Uganda specifically)

Funding isn’t the goal, but it’s a strong indicator of what the market believes will work.

Thunder Code’s early raise suggests three things that matter for Uganda’s mobile ecosystem:

1) Practical AI beats flashy AI

Investors and customers are leaning toward AI that saves time and money on a known bottleneck. QA is a bottleneck everywhere.

Ugandan providers should take note: AI adoption sticks when it fixes operational pain, not when it’s added as marketing.

2) Africa can build core infrastructure tools

There’s a quiet shift happening: African founders aren’t only building consumer apps. They’re building tools that other companies depend on—testing, security, analytics, compliance.

That’s good news for Uganda because it increases the odds of:

• regional pricing models that fit local budgets
• better understanding of African device realities
• products designed for markets with network variability

3) QA is becoming a competitive advantage in fintech

In 2025, speed matters—but reliability matters more.

If your competitor releases features weekly but breaks payment flows twice a month, users notice. Merchants notice even faster.

AI-powered QA agents offer a credible path to: ship faster while breaking less.

“People also ask” (quick answers Ugandan teams need)

Will AI testing agents replace QA engineers?

No. They replace repetitive execution, not judgment. The winning setup is AI agents + human QA + strong product ownership.

Is AI QA useful if we already have automated tests?

Yes, because most automated tests validate logic, not lived experience. AI agents are better at UI/UX issues, flow friction, and “this button is there but unusable.”

What’s the first metric to track after adopting AI-powered QA?

Track post-release defect rate (bugs found in production) and time-to-detect. If both improve, the approach is working.

Does this matter for small Ugandan startups?

Even more. Small teams can’t afford long QA cycles or expensive production incidents. Automating core flows gives you speed without chaos.

What to do next (if you run a mobile or fintech product in Uganda)

If you’re building mobile financial services, agent banking apps, merchant tools, or any high-transaction mobile product, AI-powered QA agents are one of the most sensible AI investments you can make in 2025. They target a painful, measurable problem: slow testing and avoidable production defects.

As you follow this Enkola y’AI Egyetonda Eby’obusuubuzi n’Okukozesa Ensimbi ku Mobile mu Uganda series, keep one principle in mind: AI should protect trust before it chases novelty. Trust is what keeps users transacting.

If Thunder Code can build AI agents that consistently catch the issues your customers complain about—before release—what else could Ugandan teams automate next: fraud monitoring, customer support triage, or reconciliation?