Amit Patole

Platform architect.
AI systems engineer.

I started my career as one of the first engineers at a small firm in Pune, sourcing hardware, designing the network, and building production infrastructure from scratch. That experience of building from nothing shaped how I work: understand the foundation before you touch the surface, and never call something done until you can prove it holds.

Over the past twenty years I’ve worked across the full stack of platform engineering: build-and-release systems, carrier-grade private cloud, and Kubernetes fleets serving hundreds of microservices across global datacenters. At Viavi Solutions I serve as the primary cloud and platform architect for telecom systems deployed with major operators across multiple continents. I’ve shipped AI tools now part of the Viavi AI Assistant product range, led teams spanning multiple time zones, and built the infrastructure automation that turns slow, error-prone release cycles into something a team can actually rely on. When something breaks in production, I’m the escalation lead.

Working deep in AI platforms eventually crystallized a question I had been circling for years: when a model produces an output, what does it actually mean for the work to be done? Not that a pipeline succeeded or a test passed. Done in the sense that you’d stake your name on it. A 2026 paper I published explored one edge of that question, asking whether quantum-optimization can find routing solutions across a multi-agent LLM cascade that classical greedy approaches miss.

The open-source work below is the engineering answer to the same question. The Verel organism and its standalone companions are built on one conviction: nothing compounds until a grader returns a verdict.

Publications

2026

VANTAGE: AI Security Observability, Blast Radius, and Guarded Mitigation

Technical Design Document v1.0 · Zenodo, August 2026

10.5281/zenodo.21743977

2026

VR Time Travel: A Personal Nostalgia Engine with Real-Time AI Scene Correction

Zenodo, July 2026

10.5281/zenodo.21343168

2026

Attested Test Selection with a Verifiable Confidence Bound

Zenodo, July 2026

10.5281/zenodo.21231687

2026

Quantum-Enhanced LLM Cascade Routing: A QAOA Approach to Cost-Optimal Model Selection in Multi-Agent Systems

Preprints.org (MDPI AG), April 2026

10.20944/preprints202604.0413.v1 · Zenodo

2026

Grounded Cognitive Architecture for Enterprise AI Agents: Eliminating Hallucination via Mandatory Tool Execution at Scale

Zenodo, March 2026

10.5281/zenodo.19255196

Software

2026

latenzy: per-model LLM latency monitoring

Software release v0.2.0 · Zenodo, August 2026

10.5281/zenodo.21799202

2026

assaylab: validation intelligence for CI

Software release v0.3.0 · Zenodo, July 2026

10.5281/zenodo.21230859

github.com/amitpatole linkedin.com/in/amit-patole amit.patole@gmail.com

Verified AI, built in the open — a family of tools where nothing is “done” until a grader returns a verdict.

The Verel organism

Verel brain

Verified agents: nothing is “done” until a grader returns a verdict — verification, compounding memory, a fleet, agent-run CI/CD.

docs · GitHub

AgentVision eyes

Eyes for AI agents: a machine-graded visual feedback loop (DOM/contrast/OCR-grounded) agents consume to self-correct before claiming done.

docs · GitHub

Audel ears

Ears for AI agents: a machine-graded audio/voice feedback loop, grounded in a verdict on the produced sound.

docs · GitHub

Vitel vitals

Vitals for AI agents: graded observability returning pass/warn/fail on metrics, SLOs, and error budgets, grounded in a Metric.

docs · GitHub

Projects

latenzy llm latency

Per-model LLM latency monitoring — a Prometheus exporter and Grafana dashboards for Claude, OpenAI, and Gemini. Model A vs model B, not lab-level.

docs · GitHub

Tessera attested analytics

Attested NL→analytics for regulated finance: every number ships with the executed SQL, a runtime verdict, and a signed, auditor-verifiable receipt.

docs · GitHub

assaylab validation intel

Validation intelligence: attested test-selection with verifiable confidence bounds, failure-signature RCA, and CI analytics.

docs · GitHub

gapcert airgapped tls

Publicly-trusted TLS certificates for airgapped and egress-restricted fleets — an ACME DNS-01 broker, agents, and offline courier mode.

docs · GitHub

gapsign airgapped signing

Container-image signing for airgapped and egress-restricted fleets — sign at CI publish or mirror ingest, enforce at Kubernetes admission via Kyverno, all offline (no Fulcio/Rekor).

docs · GitHub