BharathStaff AI Engineer · Production Multi-Agent Platforms
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Enterprise AI platform · 2025 –

Agentic Browser Automation at Scale

Senior AI Engineer, Platform

Browser automationCDPHuman-in-the-loopProduct enablement

Challenge

Internal product teams needed browser automation agents that could run reliably at scale: distributed sessions, CDP control, and human-in-the-loop when flows broke. A major revenue line was stuck behind manual workflow gaps.

Approach

Tech stack

CDP · Distributed browser sessions · Agent runtimes on Kubernetes · Streaming execution · HITL workflow patterns · TypeScript agent SDK

Outcomes

Verifiable patterns (redacted)

Session pool architecture (redacted)

Distributed CDP session manager: browser pods on Kubernetes with session affinity, idle timeout, and max concurrent tabs per workflow. Session lease prevents double-assignment; stale sessions reclaimed via heartbeat watchdog.

HITL checkpoint flow (redacted)

When automation hits an unrecoverable DOM state, workflow pauses and surfaces a human-in-the-loop checkpoint with screenshot, DOM snapshot hash, and suggested recovery actions. Operator resolves or aborts; trace continues with annotated decision for eval replay.

Platform execution hook (redacted)

Product teams register browser flows via agent manifest: CDP steps, retry policy, HITL triggers, and eval golden paths. Execution plane spins ephemeral browser pods with platform auth — no one-off scripts outside the standard runtime.

Context

Internal product organization (customer and product names anonymized).