6 min read
The bug you didn't find in testing is the one your user finds at 2pm on a Friday.
QA Culture · Process
~/iflux/QA-and-Testing
~/iflux/QA-and-Testing$_run_suite --mercy=none
We break things on purpose, in code and in conversation. So your users never have to.
QA & testing means making sure your product, and every AI agent inside it, breaks in testing, not in front of your customers. We cover functional testing, AI agent testing, performance under real load, security, and mobile, built into how software gets shipped, not added as an afterthought once it's already live.
// quality isn't a phase at the end.
It's the contract we sign at the beginning.
Get in touch →It's the contract we sign at the beginning.
_what is AI agent testing// the questions
// traditional QA
// was never built to ask
// traditional QA
// was never built to ask
What is AI agent testing?
AI agent testing means validating what an AI agent actually does in production, not what it was designed to do: catching hallucinated answers, guardrail bypasses, tool calls with the wrong arguments, and behavior that changes the moment you upgrade the underlying model.
- →Hallucinations: confident, fluent, and wrong, now answering your customer directly
- →Guardrail bypasses: the boundary that held in testing, until an adversarial prompt found the gap
- →Tool-call failures: the agent calls the right function with the wrong arguments, and nothing errors
- →Model drift: an answer that passed last week, failing after a routine model upgrade
_what we hunt for// the failures
// your happy path
// never tests
// your happy path
// never tests
- ⚠Race conditions: the bug that only exists when two users act simultaneously
- ⚠Memory leaks: the ones that compound silently until production falls over at 3am
- ⚠Auth edge cases: tokens that don't invalidate, sessions that persist when they shouldn't
- ⚠The API call that works until two users hit it at once: silent failure, confident 200 response
- ⚠The mobile screen that breaks on the one device your team didn't test with
- ⚠The migration that passed staging and failed production, because staging wasn't production
- ⚠The feature that worked perfectly, in the timezone you tested it in
- ⚠The performance cliff: everything fine until 8,000 concurrent users, then everything isn't
_what we test
_AI systems
/01
AI Agent & LLM Testing
We test what your AI agent does when nobody's watching: hallucinated answers, guardrail bypasses, tool calls with the wrong arguments, and behavior that drifts the moment you swap in a new model.
/02
AI-Powered Test Automation
Suites that generate themselves from your user flows and repair their own selectors when the UI changes, so automation doesn't quietly rot the week after it ships.
_software systems
/03
Visual Regression & Cross-Browser Testing
Every release checked pixel-for-pixel across Chromium, Firefox, and WebKit, so a layout fix in one browser doesn't ship as a broken page in another.
/04
Performance & Load Testing
We find your breaking point before your users do. At 10x your expected load. Then we help you fix it, before it's an incident.
/05
Security Testing & Penetration Testing
We think like attackers so your actual attackers can't find what we already found and fixed.
/06
API & Contract Testing
Services that work in isolation but fail together are the most expensive kind of failure. We test the contracts, not just the endpoints.
/07
Mobile Testing
Real devices. Real networks. Real conditions. Not just the simulator on the MacBook that ships it.
/08
QA Strategy & Process Design
For teams who have testing but don't have confidence. We build the system, the culture, and the coverage, not just the test cases.
_a closer look — catching a hallucinated answer
Assistantonline
What's your refund policy for annual plans?
9:41 AMWe offer a 90-day money-back guarantee, no questions asked.
9:41 AM_iflux qa layer — not shown to the end user
[██████████████████░░] 91%
⚠ hallucination detected: no matching policy on file.
Can I export my data if I cancel?
9:43 AMYes — export as CSV or JSON any time from account settings.
9:43 AM_iflux qa layer — not shown to the end user
[███████████████████░] 97%
✓ verified against Data Export Policy §2.
Most teams test to confirm it works.
We test to find out where it doesn't.
// those are two completely different mindsets.
// only one of them finds the bug before your user does.
// mindset
We test like attackers.
We approach every system looking for the thing that shouldn't be possible, because that's exactly what an attacker or an edge-case user will find.
// method
We report like engineers.
No vague “it broke here.” Every issue is documented with reproduction steps, environment context, severity rating, and a recommended fix path.
// relationship
We fix like partners.
We don't hand a list of bugs to your dev team and disappear. We stay in the room until the fix is shipped, re-tested, and confirmed in production.
_what a real test run looks like
iflux: qa_full_suite.sh --mercy=none
~/iflux/QA $ run --suite=full --env=production-mirror --mercy=none
→ Running unit tests (1,847 cases)...
✓ 1,847 passed. 0 failed.
→ Running integration tests...
⚠ Auth token not invalidated on logout. Active session persists. Security risk.
→ Running load test at 10x expected traffic...
⚠ Response time degrades 340% above 8,000 concurrent users.
⚠ Memory leak in session handler. Compounds over time. Will fail at ~72hrs uptime.
→ Running cross-device mobile suite (34 devices)...
⚠ Layout breaks on Android 12 / Samsung One UI 4. Affects 23% of target market.
→ Running API contract tests...
⚠ Payment service returns 200 on timeout. Silent failure in production.
→ Running AI agent test suite (adversarial + functional)...
⚠ Hallucinated answer detected: agent cited a refund policy that doesn't exist.
⚠ Guardrail bypass: agent disclosed internal pricing logic under an adversarial prompt.
→ Running security penetration scan...
✗ SQL injection vector found in search endpoint. Severity: critical.
→ Running timezone edge case suite...
⚠ Timestamp logic breaks during US DST fall-back. Duplicate 1–2am hour mis-ordered.
✓ Suite complete. 9 issues found before your users did.
→ Fix priority list generated. Severity-ranked. Reproduction steps included.
~/iflux/QA $ _
_why iFlux for QA
// quality from the start
QA built in from day one. Not bolted on at the end.
The most expensive bugs are the ones found after the architecture is set. Every Flux we embed works alongside engineering from sprint one, not after the feature is “done.”
// automation that holds
We build test suites that survive the codebase changing.
Brittle automation is worse than no automation: it breaks on every deploy and teaches teams to ignore the results. We engineer suites that stay green when the code changes correctly, and red when it doesn't.
// adversarial mindset
We test what your team assumed would never happen.
Happy path testing finds the obvious bugs. We test the edge cases, the race conditions, the malformed inputs, the third-party failures: the things that only happen in production, at scale, to the user who matters most.
// shipping fast and right
Shipping fast and shipping right aren't opposites. Skipping QA is.
The teams that skip QA to move faster are the ones who spend the next sprint fixing what shipped. We build coverage that accelerates delivery, not one that slows it.
_thinking / QA & Testing
8 min read
We ran a full security audit on a “tested” codebase. Here's what we found.
Security Testing · Penetration
5 min read
Why your test automation is green and your production is still broken.
Test Automation · Strategy
_related
QA doesn't end when the software ships. It starts when it does. The best time to build quality in is before the first line of code. The second best time is now. Both paths lead to the same place: software your team can ship with confidence.
_frequently asked
What's the difference between test automation and AI-powered testing?
Test automation engineering means writing automated test suites, scripts that run without a person clicking through the software, that keep working as the codebase changes. AI-powered testing goes further: intelligent test generation, anomaly detection, and regression coverage that adapts faster than a human writing new test cases by hand, and surfaces patterns a fixed script was never written to look for.
What is AI agent testing, and how is it different from regular QA?
Regular QA checks whether software does what it's supposed to do, deterministically: click the button, get the expected result. AI agent testing checks a system that's non-deterministic by design, so it has to validate answers against a source of truth instead of a fixed script, confirm the agent holds its guardrails under an adversarial prompt, and verify tool calls fire with the right arguments. It's testing judgment, not just logic.
We upgraded our AI model. Do we need to retest everything?
Yes, and it's the gap most teams miss. A model upgrade can change how an agent answers the exact same question, even when nothing else in the codebase changed. We rerun the full agent test suite against every model upgrade, so behavior drift shows up in a test result before it shows up in a customer conversation.
How does QA fit into a build timeline without slowing down delivery?
Quality assurance (QA) works best embedded from the first sprint, not bolted on after a feature is called done. When testers work alongside engineering from day one, defects get caught while they're cheap to fix, instead of after the architecture has hardened around them. Teams that skip QA to move faster typically spend the next sprint fixing what they shipped, so building coverage in accelerates delivery rather than slowing it.
What does load and performance testing actually involve?
We push a system to roughly ten times its expected traffic to find the point where it breaks, rather than waiting for real users to find it first. That means measuring how response times degrade as concurrent users climb, catching memory leaks that only surface after hours of sustained load, and handing back the specific point, for example the exact user count where performance falls off a cliff, so the fix happens before it's an incident.
Is security testing part of your QA process, or a separate service?
It's part of the same engagement. Security testing and penetration testing means we try to break into the software the way a real attacker would, hunting for issues like SQL injection (tricking a database into running commands it shouldn't) or authentication tokens that don't properly log a user out. We think like attackers so the people actually trying to attack your system find nothing we haven't already found and fixed.
Our test automation suite is green but bugs still reach production. What's going wrong?
Green tests don't guarantee safety if the suite only checks the happy path, the scenario where everything goes right. Bugs reach production through race conditions, malformed inputs, and third-party failures that happy-path tests never exercise. We build automation that also covers those edge cases and stays reliable as the codebase changes, so it turns green when the code is actually correct and red when it isn't, instead of breaking on unrelated changes and training teams to ignore failures.
Something in your stack is broken.
You just haven't found it yet.
Let's find it first.
// every production system has something.
// a Flux who finds it for you is cheaper
// than a user who finds it for themselves.