Autonomous Development & Testing
Coming Soon — V2.00: MCP integration is coming as part of Kubeshark V2.00. Read the announcement.
Kubeshark doesn’t write code—it bridges the last mile to production.
AI coding assistants (Claude Code, Cursor, Copilot) can write and deploy code, but they lack visibility into how that code actually behaves in a Kubernetes environment. Kubeshark closes this gap by providing real-time network feedback, enabling AI tools to identify issues and fix them before releasing to production.
Instead of “deploy and pray,” you get “deploy, verify, and fix”—all in one autonomous loop.
The Problem: The Last Mile Gap
AI coding tools can write sophisticated code, but they operate blind when it comes to production behavior:
- No visibility — AI generates code but can’t see how it behaves in a real cluster
- Delayed feedback — Issues surface in production, long after the code was written
- Limited context — Unit tests pass, but integration failures hide in network interactions
- Manual verification — Developers must manually check logs and traces to verify behavior
The result: code that “works” in isolation but fails in production. AI tools keep repeating the same integration mistakes because they never see the actual network behavior.
How Kubeshark Bridges the Gap
Kubeshark provides the missing feedback loop. When connected to an AI coding assistant via MCP, it enables the AI to:
- See actual behavior — Every API call, payload, header, and response
- Identify issues — Malformed requests, missing headers, unexpected calls
- Correlate cause and effect — Link code changes to network behavior
- Iterate until correct — Fix issues and re-verify automatically
+-----------------------------------------------------------------+
| CLOSED-LOOP DEVELOPMENT |
| |
| +-----------+ +-----------+ +-----------+ |
| | AI Writes |---->| Deploy |---->| Test | |
| | Code | | to K8s | | | |
| +-----------+ +-----------+ +-----+-----+ |
| ^ | |
| | v |
| +-----+-----+ +-----------+ |
| | AI Fixes |<----------------------| Kubeshark | |
| | Issues | Network Feedback | MCP | |
| +-----------+ +-----------+ |
| |
| Kubeshark provides the production insight AI tools lack |
+-----------------------------------------------------------------+Example prompts:
“Deploy my changes to the local cluster and verify the new /api/orders endpoint works correctly.”
“Run the integration tests and use Kubeshark to verify the API calls are correct—check payloads, headers, and downstream calls.”
“Test my authentication middleware. Verify it properly rejects invalid tokens and check what headers it adds to valid requests.”
“I changed the retry logic. Deploy and verify that failed requests are retried exactly 3 times with exponential backoff.”
What Network Feedback Catches
Kubeshark provides feedback that logs, metrics, and test assertions miss:
| Traditional Testing | Network-Level Verification |
|---|---|
| “Test passed (200 OK)” | “Response was 200, but payload had wrong field types” |
| “No errors in logs” | “3 unexpected retries occurred, masking a connection issue” |
| “Latency within SLA” | “Latency is fine, but you’re making 5 redundant DB calls” |
| “Auth test passed” | “Auth works, but token is sent to downstream services unmasked” |
Specific issues the AI catches:
- Malformed payloads that “work” but violate API specs
- Extra API calls that shouldn’t happen (N+1 queries)
- Missing or incorrect headers (auth, correlation IDs, content-type)
- Serialization bugs (wrong date formats, encoding issues)
- Downstream services called with wrong parameters
- Retry/timeout logic that doesn’t behave as intended
- Connection pooling issues
The Autonomous Loop in Action
+---------------------------------------------------------------------------------+
| Terminal - claude |
+---------------------------------------------------------------------------------+
| |
| > I've updated the order-service to add caching. Deploy it and verify |
| it works correctly. |
| |
| * Building and deploying order-service |
| - Running unit tests... 23 passed |
| - Building container image... |
| - Deploying to local cluster... |
| - Waiting for rollout... Ready |
| |
| * Running integration tests with network verification |
| - Executing test suite... |
| - Creating Kubeshark snapshot... |
| - Analyzing 847 API calls... |
| - Verifying network behavior... |
| |
| ! Issues detected: |
| |
| 1. Cache miss on every request |
| Expected: First request hits DB, subsequent requests use cache |
| Actual: All 12 requests to /api/orders/{id} called the database |
| Evidence: 12 calls to postgres-service for same order ID |
| |
| 2. Missing Cache-Control header |
| Expected: Response includes Cache-Control: max-age=300 |
| Actual: Header not present in any response |
| |
| Root cause: The cache key includes a timestamp, making every lookup unique. |
| |
| Suggested fix: Remove timestamp from cache key in order-service/cache.go:47 |
| |
| > Yes, apply the fix |
| |
| * Applying fix |
| - Updating order-service/cache.go... |
| - Rebuilding and redeploying... |
| - Re-running verification... |
| |
| * Network verification results |
| - First request: DB call OK |
| - Subsequent requests: Cache hit (no DB call) OK |
| - Cache-Control header present OK |
| - Response times: 45ms (cold) -> 2ms (cached) OK |
| |
| OK All verifications passed. Your caching implementation is working correctly. |
| |
| > _ |
| |
+---------------------------------------------------------------------------------+Another Example: Verifying Retry Logic
+---------------------------------------------------------------------------------+
| Terminal - claude |
+---------------------------------------------------------------------------------+
| |
| > I implemented retry logic for the payment service. It should retry |
| 3 times with exponential backoff (1s, 2s, 4s). Deploy and verify. |
| |
| * Deploying and testing retry logic |
| - Deploying payment-client... |
| - Configuring payment-service to fail first 2 requests... |
| - Triggering test payment... |
| - Analyzing retry behavior... |
| |
| ! Retry logic has issues: |
| |
| Expected: 3 retries with 1s, 2s, 4s delays |
| Actual: 3 retries with 1s, 1s, 1s delays |
| |
| Network evidence: |
| Request 1: 14:23:15.000 -> 503 Service Unavailable |
| Request 2: 14:23:16.012 -> 503 Service Unavailable (delay: 1.01s) |
| Request 3: 14:23:17.018 -> 503 Service Unavailable (delay: 1.00s) |
| Request 4: 14:23:18.025 -> 200 OK (delay: 1.00s) |
| |
| The backoff multiplier isn't being applied. |
| |
| Found in payment-client/retry.go:23: |
| delay := baseDelay // Should be: delay := baseDelay * (1 << attempt) |
| |
| > Fix it |
| |
| * Fixing and re-verifying |
| - Applying fix to retry.go... |
| - Redeploying... |
| - Re-running test... |
| |
| OK Retry logic verified: |
| Request 1: 14:25:00.000 -> 503 (initial) |
| Request 2: 14:25:01.005 -> 503 (delay: 1.00s) OK |
| Request 3: 14:25:03.011 -> 503 (delay: 2.00s) OK |
| Request 4: 14:25:07.018 -> 200 (delay: 4.00s) OK |
| |
| > _ |
| |
+---------------------------------------------------------------------------------+Why This Matters
Kubeshark transforms AI coding tools from “code generators” into “production-ready code generators”:
| Without Kubeshark | With Kubeshark |
|---|---|
| AI writes code, hopes it works | AI writes code, verifies it works |
| Issues found in production | Issues found before commit |
| Manual debugging after release | Automatic detection and fix |
| “Works in tests” ≠ works in prod | Verify actual production behavior |
Key benefits:
- Shift-left production visibility — Catch integration issues before they reach staging
- Autonomous verification — AI handles the test-analyze-fix cycle without manual intervention
- Evidence-based fixes — AI sees the exact network behavior causing issues
- Confidence to release — Code is verified against real Kubernetes behavior, not mocks
The Complete Picture
What AI coding tools do:
- Write and modify code
- Deploy to Kubernetes clusters
- Run tests and trigger API calls
- Apply fixes based on feedback
What Kubeshark provides (via MCP):
- Real-time visibility into every API call
- Full request/response payloads and headers
- Timing data and latency analysis
- Evidence of actual behavior vs expected behavior
- Detection of issues AI tools can’t see otherwise
Together: The AI writes code, Kubeshark shows what that code actually does, and the AI fixes issues based on real network evidence—all in one continuous loop.
What’s Next
- Conversational Debugging — Debug integration issues
- AI Use Cases — Common scenarios for AI-powered analysis
- MCP in Action — See a complete investigation example