Label, train, and deploy
AI browser agents
Training LLMs is solved. Training BROWSER AGENTS? That's the frontier. The only platform providing training data, validation, and deployment for browser, desktop, and GUI agents.
Browser agents fail. You have no idea why.
Building browser agents without visibility, training data, or benchmarks means shipping blind and improving by luck.
Failures are opaque
Are your browser agents getting stuck on captchas or silently failing on page loads? Without action-level tracing across browser sessions you're debugging blind, guessing at prompts, and hoping the next run works.
No training data
No way to capture what your agent actually did, label which actions were correct, or build datasets from real sessions. You're improving by instinct, not evidence.
Improvement is unmeasurable
You change the prompt. Is the agent better? Worse? There's no baseline. No version history. No way to A/B test GPT-4 against Claude on the same browser workflow. "V2 feels worse" is not a metric.
Built by the team behind Debugg.ai — 800+ users, 10,000+ agent tests/week
The complete lifecycle for action-based agents
Label action sequences. Train on real interactions. Validate multi-step flows. Deploy with confidence.
Label
Record and annotate agent actions automatically. Capture every action in the flow. Label success/failure at action-level. Build datasets of action sequences.
Train
Training data for action sequences (not just text). Clean, labeled datasets ready for fine-tuning. RLHF workflows for agent behavior. We're creating CommonCrawl for actions.
Deploy
Sandbox → Staging → Production with safety. Test in isolated environments. CI/CD for agent updates. Canary rollouts for behavior changes.
Eval
Validate 49/50 vs 50/50 correct actions. Action-level validation (not just outcomes). Behavioral testing (did it take the right path?). Version control for agent behavior.
The complete lifecycle in code
Label, train, deploy, and eval—all integrated. From action recording to production deployment in one platform.
import { Surfer, Dataset, Eval } from '@surfs/sdk'
// 1. Label: Record and annotate actions
const session = await Surfer.record({
task: "Add item to cart",
captureActions: true,
labelSuccess: true
})
// 2. Train: Build dataset from labeled actions
const dataset = await Dataset.create({
sessions: [session],
format: "action-sequences"
})
// 3. Deploy: Test in sandbox before production
await Surfer.deploy({
environment: "sandbox",
canary: 0.1 // 10% rollout
})
// 4. Eval: Validate 50/50 actions, not 49/50
const results = await Eval.run({
validateEachAction: true,
compareToBaseline: true
})The complete agent lifecycle: Label → Train → Deploy → Eval, all in one SDK.
See what your agent is thinking
Every action starts with an LLM decision. Track reasoning → execution, not just raw actions. Debug failures at the decision level.
run_a3f9e2b8 · 47.3s · 12 steps
goto("https://shop.example.com")click("#search-button")fill("input[name=q]", "red t-shirt")click(".add-to-cart-btn")Latest Resources
Stay updated with the latest insights, guides, and best practices for AI-powered development and testing.
Synthetic Web Farms for Browser Agents: Procedural UI Generation, Ground-Truth Tasks, and Hermetic Training Pipelines
Build a synthetic web farm for AI browser agents: procedurally generate diverse UIs (forms, carts, tables, shadow DOM), embed ground-truth task graphs, auto-label DOM/event traces, randomize skins, and export hermetic snapshots for RLHF/OPE.
Speculative Multiverse Planning for Browser Agents: Parallel Tabs, Budget‑Aware Pruning, and Deterministic Safe‑Merge
A technical blueprint for building browser agents that explore action branches in parallel tabs, score them with causal metrics under cost and latency budgets, and deterministically commit a single path with idempotent writes and replayable audit logs.
Consent‑Aware Browser Agents: TCF 2.2/US Privacy Strings, CMP Negotiation, and Dark‑Pattern‑Resistant Pipelines
A technical blueprint for building browser agents that legally and predictably handle consent: parse TCF 2.2 and US Privacy strings, negotiate CMP UIs, persist per‑origin preferences, detect dark patterns, test across vendors, and train policies that minimize data collection.
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