For teams shipping with coding agents
Your AI agents write the code. Who makes sure it won't break production?
Run Claude Code, Cursor and Codex, ship code like crazy, and we will make sure nothing breaks: no regressions, no latency or performance degradation across your APIs, databases and network calls, no broken contracts, and no new or old bugs in production.
4 dedicated QA engineers. Your repo. Your CI. First protection in 7 days. $7k/month.
We'll review your current AI development workflow and identify where independent automated testing could help.
- Existing behaviorunchanged
- API contracta field the mobile app reads was removed
- Securityno new secrets, dependencies clean
- Sign-up flowworks in a real browser
Protected in 7 days
We find your five most important user journeys and have real automated checks running on every pull request in the first week. No month-long audit first.
The whole product, not just the UI
Screens, APIs, integrations, infrastructure and any AI features each get at least one meaningful check, then deeper coverage where the risk is highest.
One dashboard you own
Every test run, every failure and its evidence in one place, so "is it safe to release?" has an answer that isn't five tools and a spreadsheet.
A dedicated team of four builds it, runs it and keeps it current as your product and your agents change. It lives in your repo and your CI, for a fixed $7k a month.
A worked example
One agent change, caught before merge.
A common failure shape, step by step. It's an example scenario, not a client story.
- 1
What the AI agent changed
Pull request: Tidy up the accounts response
GET /v1/accounts- plan_id: string;+ plan: { id: string };
The agent renamed one field to match the new schema. The diff looks clean, and the unit tests it wrote itself still pass.
- 2
What the harness caught
Check failedcontract: GET /v1/accountsexpected field plan_id (string)got undefinedstill reading it: mobile app 4.2, billing webhook
A contract check built from how your product behaves, not from the agent's diff, found two things still reading the old field.
- 3
What happened before merge
- Blocked. The pull request could not merge.
- Sent back. The failure went to the agent as a comment it could act on.
- Fixed. It kept plan_id and added plan.id beside it. Re-run: green.
- Merged. Your engineers never had to read the diff. Customers saw nothing.
Your first 30 days
First protection in 7 days. The rest of the product by the end of the month.
We don't start with a month-long audit. We begin protecting your real product while we learn it: your five key journeys first, then at least one meaningful check across everything, then depth where the risk is highest.
Where most startups start
- Thin unit tests, and few or no integration or end-to-end tests
- A CI that skips the tests, or a suite that takes hours
- No load tests, and monitoring or alerts nobody owns
- No clear escalation path when something breaks
- No single place to see all of it
We work through each of these in the first 30 days.
Week 1: first protection
Day 1Access and onboardingWe meet your engineering lead and get safe access to everything we need.
- We learn your architecture, business workflows, release process and known production failures
- Least-privilege access to your repos, CI/CD, staging, API specs, test infrastructure and existing monitoring
- We agree data-handling boundaries and the test accounts we will use
Day 2Product and test discoveryWe find out what you have, what runs, and what actually breaks.
- We inventory modules, services, critical APIs, existing tests, CI workflows, environments and recurring failures
- We run your current suites for a baseline: duration, pass rate and flakiness
- We note the biggest gaps in coverage
Day 3Choose your five journeysFive business-critical workflows, agreed with you. Not five easy tests.
- Usually sign up, log in, the one action your product exists for, billing or subscription, and retrieving saved data
- We map the screens, APIs, data and outside services behind each one
- You approve the list before we build anything
Days 4 to 5Build the first protectionReal checks for each journey, built to be reliable.
- Dedicated test accounts, fixed fixtures and isolated data
- Browser checks plus the faster, more precise API or integration checks behind them
- Every failure captured with screenshots, traces, logs and evidence you can reproduce
Days 6 to 7Connect and demonstrateResults on your real builds, in your dashboard.
- Tests wired into your CI and the first version of your dashboard
- A live demo on a real build
- Uncovered risks named, first release gates proposed, next priorities agreed
By the end of week one you have
- Five prioritised journeys, documented, with first automated checks
- A baseline of your existing tests, CI duration and biggest gaps
- First CI results, with failure evidence
- Your dashboard, live, even if only a few sources are connected
- A prioritised reliability backlog and the next milestones
These are targets. They depend on usable staging access, test data and CI permissions. If a journey is blocked by an unstable dependency, we document the blocker and build the most valuable check we can instead.
Week 2 and beyond: broader and deeper
Weeks 2 to 3Broader coverageProtection spreads across the whole product.
- High-risk APIs, integration boundaries, background jobs and the failures that matter most
- We repair unreliable tests and speed up CI. We never delete a test until we have confirmed it protects no unique behavior
- Coverage mapping in your dashboard grows with every check
Weeks 3 to 4Release assuranceA safety net for releases and for production.
- First release gates, core synthetic checks and alerts that someone owns
- Logging improvements, performance baselines and AI feature evaluations where they apply
- A short progress report every week: what is newly protected, what we repaired, what we found, what is next
OngoingContinuous defenseThe harness keeps up as your product and your agents change.
- New features get new checks, and tests are updated alongside the code
- Failures are investigated, CI is refined and observability improves
- Reliability progress is measured and reported, not assumed
SHIELD, our method
Your coding agents accelerate development. SHIELD builds the reliability layer that keeps up.
It's how we start and how we keep going: six steps we repeat as your product and your agents change. When a new feature, API, agent or piece of infrastructure appears, we run the right steps again. We don't promise 100% coverage. We promise risk-based, steadily improving checks, with clear ownership and visibility.
- S
Survey
Understand the product, architecture, journeys and existing tests.
- H
Harden
Repair unreliable tests and cut low-value testing noise.
- I
Instrument
Establish useful signals across code, APIs, infrastructure and AI.
- E
Exercise
Build independent tests for the journeys and failure modes that matter most.
- L
Link
Connect test runs, CI/CD, monitoring and reports in one place.
- D
Defend
Enforce release gates, keep coverage current and respond to regressions.
Every critical part of the product should have a meaningful safety check. Every critical user journey should be independently verified. Every release should have clear evidence of what passed, what failed, and what is still at risk.
Reliability Command Center
One dashboard. Every test run. One clear picture of release readiness.
Your repo. Your CI. Your dashboard. Not scattered across CI logs, Playwright reports, your monitoring tool and someone's laptop. It answers six questions.
Critical journeys
18/20
Verified
PR checks
4m 12s
Median duration
Reliability gaps
7
Open priorities
Coverage by product surface
- Critical user journeys90%
- Core APIs72%
- Third-party integrations58%
- Infrastructure checks80%
- AI feature evaluations45%
Recent execution results
- Login E2EPassedPR #482
- Billing API contractFailedPR #482
- AI tool-call evaluationPassedPR #481
- Production syntheticPassed5m ago
Example view with illustrative data. Coverage percentages are weighted coverage of inventoried obligations, not code coverage.
What's protected?
A living map of your modules, APIs, journeys and infrastructure, and how well each is covered.
What just ran?
Every execution with its commit, branch, environment, duration and result.
What failed, and why?
Evidence, screenshots, trace links, affected components and a suspected cause.
Can we release?
Required gates, known failures and untested changes in one view.
What's happening in production?
Synthetic checks and key alerts, linked to your own monitoring tools.
Are we getting safer?
Trends in coverage, escaped defects, flaky tests and execution time.
Coverage maps, release readiness and trends are part of it. It preserves your source reports and links back to them, so you keep every tool you already use.
How it fits together
Results from the tools you already use flow into one place you own, and back out to your engineers and your coding agents.
- GitHub / GitLab CI
- Playwright / Pytest / Jest
- k6 / Security scanners
- New Relic / Datadog
- AI evaluation suites
- Production synthetics
SHIELD ingestion layer
Adapters, normalized results, historical events and artifact links.
Customer-owned Reliability Command Center
Coverage, runs, failures, risk, release gates and trends.
Engineers and coding agents
Actionable failures, PR comments, issues and remediation feedback.
Instrument
Monitoring, logging and incident readiness.
Testing alone doesn't tell you when something fails in production. So the harness includes a minimum viable reliability monitoring layer, built on the tools you already run.
- Structured logs with correlation IDs and useful error context
- Critical service metrics: availability, errors, latency and resource saturation
- Traces across priority workflows where supported
- Synthetic checks for login and core product functionality, using safe production test accounts
- Actionable alerts with severity, routing, runbook links and acknowledged owners
- An escalation matrix and an incident-response handoff procedure
We adopt your existing New Relic, Datadog, Grafana, CloudWatch or other tooling rather than replace it without a reason. OpenTelemetry gives us a vendor-neutral foundation for logs, metrics and traces.
Alerts focus on symptoms that affect users and on meaningful error-budget consumption, not on every isolated CPU spike.
Where the line is. PI Labs owns the testing and reliability-harness work. You retain production incident command and infrastructure operations, unless we agree a separate managed on-call arrangement.
Your team
Four people, four jobs. Not four generic testers.
The pod is organised around complementary roles, and it stays with your product as it changes.
QA Architect and Lead
Discovery, the risk register, test strategy, release gates, and your main point of contact.
Automation Engineer, UI and end-to-end
Critical journeys, browser tests, fixtures and functional regression.
Automation Engineer, API and integration
Unit-test improvements, API, contract, integration, database and background-job checks.
Reliability Engineer
CI speed, performance, security automation, synthetic monitors and dashboard integrations.
How we're different
The usual answers each leave a gap.
Here's what teams try first, where it falls short, and what we do instead.
Instead of
Agents testing their own work
The gap
An agent that writes the change also writes its tests, so the tests tend to confirm the change, mistakes included.
What we do
We write the checks from how your product should behave and what it promises other services, not from the agent's diff. A wrong change can't write its own passing test.
Instead of
CI plus a few scanners and frameworks
The gap
Each tool watches one layer and nobody owns the seams between them. You get green builds that still ship a broken signup flow or fall over under load.
What we do
One team builds and owns every layer as a single gate: code and API contracts, real-browser UI flows, security, load, and chaos tests on your infrastructure. One pass or fail per pull request.
Instead of
QA vendors and extra hires
The gap
A lot of outsourced QA is people clicking or UI scripts on a human schedule, built for weekly releases, not for agents opening ten pull requests a day.
What we do
Automated checks run on every pull request in your CI. Failures are written so the agent can read them and fix its own change. The tests live in your repo, so you own them.
No gate catches everything. What we commit to is that every bug that slips through becomes a permanent check, so the same mistake doesn't ship twice.
For the technical reader
A harness you can read, not a black box.
Everything lands in your repo and your CI: tests you can read, security checks you can audit, coverage and load numbers you can hold a release to, and failure experiments that prove your infrastructure recovers.
- Regression
- Security
- API & contract
- UI end-to-end
- Load & performance
- Chaos
- Integration
- Unit & component
Four layers, so feedback stays fast
Instead of one enormous CI job, checks are split by speed, cost and risk.
- Layer 1
PR Guard
BlockingTarget: 2 to 5 minutes
Lint and type checks, fast unit tests, API contracts, targeted integration tests, security scans and critical smoke checks.
- Layer 2
Merge and Deployment Guard
Blocking where justifiedTarget: 5 to 15 minutes
Broader integration tests, critical end-to-end journeys, migration checks, deployment smoke tests and essential AI regression checks.
- Layer 3
Scheduled
Non-blocking by defaultHourly, nightly or weekly
Full regression, browser compatibility, deeper security analysis, extensive AI evaluations, and load, stress and soak tests.
- Layer 4
Production Watch
ContinuousNear real time
Synthetic checks on key journeys, availability, error rates, latency, saturation, and alerts with clear escalation routes.
Targets, not guarantees. A large legacy monolith may need a different baseline, and riskier changes can trigger a broader suite before merge.
How we choose what to test next
Every journey, component and missing check goes into a Risk Coverage Register, scored from 1 to 5 on four factors.
- Business impactweight 40%
- Change frequencyweight 20%
- Failure likelihood and historyweight 20%
- Detection gapweight 20%
Priority score
88/100
Example risk
We refine the weights with your incident data and engineering feedback, and tag each risk with the check type, cost and effort needed. High-priority gaps with cheap fixes get done quickly, and failures involving payments, security or data loss can override the numbers.
What the Command Center is built on
| Component | What we use |
|---|---|
| Frontend | React and TypeScript |
| Backend | Node.js or Python |
| Database | PostgreSQL |
| Report ingestion | CI webhooks, JUnit XML and JSON adapters |
| Artifacts | Customer-owned S3 or GCS-compatible storage |
| Dashboards | Reusable frontend with configurable modules |
| Authentication | Customer SSO or managed identity |
| Integration | Read-only connectors wherever possible |
| Hosting | Your cloud account, or a customer-controlled dedicated deployment |
You own your test code, data, execution history, configurations and deployed dashboard. PI Labs maintains the standard integration software under agreed license terms. It is built once as a reusable platform and configured per customer, and it preserves your source reports and links back to them.
The outputs below are generated from our harness template. They are not client data.
import { test, expect } from '@playwright/test'; test('paid order shows confirmation', async ({ page, request }) => { // seed through the API so the UI test stays fast const { id } = await createOrder(request, { plan: 'pro-annual' }); await page.goto(`/checkout/${id}`); await page.getByLabel('Card number').fill('4242 4242 4242 4242'); await page.getByRole('button', { name: 'Pay now' }).click(); await expect(page.getByText('Order confirmed')).toBeVisible(); await expect(page).toHaveURL(/\/orders\/\w+/);});- Secrets scan0 findings in the diff
- Dependency auditlodash 4.17.15 has known vulnerabilities (high)
- Authorization18 of 18 routes reject anonymous requests
- Hostile inputs42 injection-style payloads handled
- Config changesno new public buckets or open ports
Merge blocked: upgrade lodash to 4.17.21, then the agent re-runs the gate.
%
overall line coverage
- src/billing/invoice.ts96%
- src/auth/session.ts93%
- src/orders/create.ts90%
- src/api/webhooks.ts71%
Gap flagged: webhook handlers are the least covered file, so they go first in the next Build pass.
POST /api/orders · ramp to 200 virtual users · 60s
budget 250 msp95 latency
- p95
- 184 ms
- p99
- 312 ms
- errors
- 0.02%
- throughput
- 1,840 req/s
- http_req_duration p(95) < 250ms
- http_req_failed rate < 0.1%
Kill 1 of 3 checkout-api replicas under steady load
error ratefault injected
- fault
- 1 of 3 replicas killed
- peak errors
- 0.4%
- recovery
- 9 s
- objective
- under 30 s
- steady state held before the fault
- recovered within the 30 s objective, no orders lost
What “protected” means for each part of your product
| Part of the product | Minimum meaningful protection | Where we go deeper |
|---|---|---|
| Frontend and UI | Critical journeys and smoke tests | Component, accessibility, cross-browser and visual regression |
| Backend APIs | Health, authentication and core business operations | Unit, integration, negative cases, schema and contract tests |
| Database | Read and write integrity, critical migrations | Rollback, concurrency, consistency and backup-restore tests |
| Third-party services | The essential happy path and failure handling | Contract tests, timeouts, retries and idempotency |
| Background jobs | Job completion and failure reporting | Retry logic, dead-letter queues and duplicate-event handling |
| Infrastructure | Deployment health and essential resource alerts | Capacity, scaling, disaster recovery and failure experiments |
| Security | Dependency and secret scans, basic authorization checks | Static and dynamic analysis, access-control matrices, adversarial testing |
| Performance | Baseline latency for important workflows | Load, stress, soak and capacity tests |
| Production monitoring | Synthetic checks for critical journeys | SLOs, error budgets, escalation and incident workflows |
| AI features and agents | Representative evaluation cases and behavior checks | Prompt-injection resilience, tool-use correctness, safety and quality regression |
A green check means a real risk is being checked, not that a whole area is exhaustively tested. Dependency scanning alone, for example, does not make an application secure.
Questions we get
We already have CI and some tests. Why a harness?
CI runs checks. A harness decides whether a change is safe. Most test suites grew by accident, so they miss what actually hurts: contracts between services, authorization paths, performance cliffs. We design the checks around your real risks, wire them into CI, and keep them current as the code changes.
Can't the coding agent just write its own tests?
It can, and it should. But an agent that writes the code and the tests is grading its own homework. The harness is independent: built by people whose job is to break the change, and it doesn't care whether an agent or a person wrote it.
What stops an agent from weakening a failing test to get a green check?
The harness is protected like production code. Changes to the checks themselves need human approval, and edits that loosen an assertion or skip a gate are flagged instead of waved through. An agent can fix its own change. It can't edit its way to green.
Will it really catch security problems?
It catches the classes of problem you can test for: leaked secrets, vulnerable dependencies, missing authorization checks, hostile inputs, risky config changes. It isn't a penetration test and it doesn't replace one. Nothing catches everything, so every escape becomes a new permanent check.
Will it slow my agents down?
Not if it's built well. Fast checks, with a target of two to five minutes, run on every pull request in parallel. Heavier ones, like broader regression, load and failure experiments, run on merge or on a schedule. The goal is a quick, trustworthy yes or no.
What does $7k a month cover?
The harness and the team behind it: four people with distinct roles who design, build and maintain it across unit, integration, API and contract, UI end-to-end, regression, security, load and chaos testing. One fixed monthly price.
What if our product is big or complicated?
A four-person team can make real progress, but it can't build deep coverage of everything at once. So we baseline first, rank the work by risk, and agree a continuously prioritised scope. We'll tell you plainly what the team can and can't cover.
Who checks the AI-written tests?
A person, every time. We use agents to draft tests faster, the same way you do. An engineer reviews and runs each one before it goes into your repo, and deletes it if it doesn't earn its place.
What do the first 30 days look like?
Week one: we get access, find out what actually breaks, agree your five most important journeys, and have first checks running on every pull request with your first dashboard. Weeks two to four: broader coverage, faster and more trustworthy CI, release gates, synthetic checks and alerts. The sections above have the detail.
Do we have to change our CI or monitoring?
We work with what you have, including New Relic, Datadog, Grafana or CloudWatch. If tests aren't running in CI we wire them in. If logging or alerts are thin, we add the basics for your critical paths and write an escalation guide. We own the testing and reliability harness. You keep incident command and infrastructure operations, unless we agree a separate on-call arrangement.
What is SHIELD?
Our method: Survey, Harden, Instrument, Exercise, Link, Defend. It's how we start and how we keep going. When new features, APIs, agents or infrastructure appear, we run the right stages again.
What happens on the 15-minute call?
A Reliability Assessment, not a pitch. We look at how your agents ship today, where you don't trust the output, and tell you honestly whether independent automated testing would help. If it wouldn't, we'll say so. No slide deck.
Find out what your agents are getting past you.
Take 15 minutes. We'll review how your code gets to production and tell you plainly if independent testing would help.
