Why CI/CD for Agents Is Different
Agent pipelines combine code, prompts, models, and datastore schemas. A silent drift in one layer can tank production quality. Manual promotion increases risk and slows iteration.
- Versioning prompts & tool definitions alongside Python code
- Validating Lakehouse access policies before deploy
- Benchmarking LLM variants on hidden test suites
- Capturing latency & cost metrics for rollback gates
Our End‑to‑End CI/CD Blueprint
1. GitOps Repo
Prompts, agent DAGs, and infrastructure‑as‑code stored in one repo with pre‑commit hooks for linting & schema lint.
2. Automated Testing
Unit tests with mocked LLMs + golden‑file evaluations. Live tests hit a staging cluster to validate policy enforcement.
3. Benchmark & Drift
PipeIQ harness benchmarks (accuracy, latency, cost) & regression drift checks before merge to main.
4. Lakehouse Promotion
LakeFlow Jobs deploy Delta configs, Unity Catalog permissions, and agent REST endpoints with blue‑green rollout & automatic rollback on SLO breach.
PipeIQ CI/CD Services
Agent Test Harness
We generate synthetic & real prompts, expected JSON outputs, and scorecards—then wire them into your pipeline.
LLM Benchmarking Lab
Compare OpenAI, DBRX, Cohere, and proprietary models across latency, cost, & accuracy on your domain data.
Pre‑Prod Risk Analysis
Static & runtime scanners catch PII leakage, policy violations, and quota risks before hitting prod.
Launch & Observability
Dashboards for success rate, hallucination score, and cost per call. PagerDuty hooks for SLO breaches.
Reference CI/CD Architecture
Want PNG / Terraform modules? Reach out.
Deploy with Confidence
Let PipeIQ design, implement, and operate your CI/CD pipeline for Databricks AI agents—so your team ships faster and safer.
