Agent Runtime

Updated 2026-01-24 - Runtime verification support for agent loops with assertion predicates and task completion tracking.

Think Jest for AI web agents. AgentRuntime provides Jest-style semantic assertions so agents can verify what changed on the page instead of guessing when they're "done".

Assertions at a Glance

Predicate provides Jest-style assertions for AI web agents.

Instead of trusting that an agent probably clicked the right thing, Predicate verifies outcomes using structured snapshots from a live, rendered browser (post-SPA hydration).

Predicate does not parse static HTML and does not rely on vision by default.

What's included

Deterministic assertions

• State-aware checks: enabled, checked, value, expanded
• Semantic selectors (no brittle CSS/XPath)
• Clear failure reasons + nearest-match suggestions

Reliability & recovery

• .eventually() retries with bounded backoff
• Snapshot confidence + exhaustion detection
• Optional vision fallback (last resort)

How it works (high level)

1. Snapshot the rendered DOM + layout from a live browser
2. Execute actions deterministically
3. Verify outcomes with pass/fail assertions
4. Retry or escalate only when the page is unstable

Vision models are used only if structural signals are exhausted — never by default.

Overview

The AgentRuntime class provides a thin runtime wrapper that combines:

• Browser session management via the BrowserBackend protocol
• Snapshot/query helpers
• Tracer for event emission
• Assertion/verification methods

It's designed for agent verification loops where you need to repeatedly take snapshots, execute actions, and verify results.

New in 2026-01-24: AgentRuntime is now framework-agnostic and accepts any browser implementing the BrowserBackend protocol. This allows integration with browser-use, Playwright, or any CDP-based browser through a single backend parameter.

Quick Start

With browser-use (Recommended)

from browser_use import BrowserSession, BrowserProfile
from predicate import get_extension_dir
from predicate.backends import BrowserUseAdapter
from predicate.agent_runtime import AgentRuntime
from predicate.tracing import Tracer, JsonlTraceSink

# 1. Setup browser-use with Predicate extension

With Builder/Pro/Teams/Enterprise Tier (Gateway Refinement)

# Same setup as above, but with API key for smart element ranking
runtime = AgentRuntime(
    backend=backend,
    tracer=tracer,
    predicate_api_key="sk_pro_xxxxx",  # Enables Gateway refinement
)

# Snapshots now use server-side ML ranking/filtering
await runtime.snapshot()

Integration with browser-use

If you're using browser-use, you have two common integration patterns:

• Backend integration (AgentRuntime-driven): use BrowserUseAdapter to turn a browser-use session into a Predicate BrowserBackend (shown above in Quick Start).
• Agent integration (browser-use-driven): use browser-use’s PredicateAgent when you want an agent loop with verification “wired in”.

PredicateAgent Integration

This runs step assertions and a done assertion, and emits verification data into traces.

from browser_use.integrations.sentience import PredicateAgent
from predicate.verification import url_contains, exists, all_of

# Define per-step assertions
step_assertions = [
    {
        "predicate": url_contains("example.com"),
        "label": "on_target_site",
        "required": True,
    },
    {
        "predicate": exists("role=button"),
        "label": "has_buttons",
    },
]

# Define task completion assertion
done_assertion = all_of(
    url_contains("/success"),
    exists("text~'Complete'"),
)

agent = PredicateAgent(
    task="Complete the checkout flow",
    llm=llm,
    browser_session=session,
    enable_verification=True,
    step_assertions=step_assertions,
    done_assertion=done_assertion,
    trace_dir="traces",
)

result = await agent.run()
print(result.get("verification"))

PredicateContext (Token‑Slasher)

PredicateContext builds compact, ranked DOM context blocks for LLMs (semantic geometry instead of full DOM + screenshots). Pair it with AgentRuntime when you want:

• Token-efficient “what the agent sees” for planning
• Deterministic pass/fail verification for progress tracking

For the full browser-use integration guide, see: Browser‑use Integration.

from predicate.backends import PredicateContext

ctx = PredicateContext(
    max_elements=60,
    show_overlay=True,
    top_element_selector={
        "by_importance": 60,
        "from_dominant_group": 15

AgentRuntime Class

Constructor

from predicate.agent_runtime import AgentRuntime

# New: Using backend parameter (recommended)
runtime = AgentRuntime(
    backend=backend,              # Any BrowserBackend implementation
    tracer=tracer,                # Tracer for event emission
    predicate_api_key="sk_...",   # Optional: Pro/Enterprise Gateway refinement

Parameters:

• backend - Any browser implementing the BrowserBackend protocol (browser-use, Playwright, CDP-based)
• tracer - Tracer for emitting verification events
• predicate_api_key (optional) - API key for Pro/Enterprise tier Gateway refinement

Backward Compatibility

For existing AsyncPredicateBrowser users, use the factory method:

from predicate import AsyncPredicateBrowser
from predicate.agent_runtime import AgentRuntime

async with AsyncPredicateBrowser() as browser:
    page = await browser.new_page()
    await page.goto

Properties

BrowserBackend Protocol

The BrowserBackend protocol defines the minimal interface required for browser integration. Any browser framework can work with AgentRuntime by implementing this protocol.

Protocol Methods

Backend Implementations

The SDK provides built-in backend implementations:

Core Methods

snapshot() - Take Page Snapshot

Takes a snapshot of the current page state. Updates lastSnapshot which is used as context for assertions.

# Take snapshot (required before element assertions)
snap = runtime.snapshot()
print(f"Found {len(snap.elements)} elements")

Returns: Snapshot - Current page state

begin_step() / beginStep() - Start New Step

Begins a new verification step. Generates a new step ID, clears previous assertions, and increments step index.

# Begin a new step
step_id = runtime.begin_step("Navigate to checkout")
print(f"Step ID: {step_id}")

# Or with explicit step index
step_id = runtime.begin_step("Verify cart", step_index=

Parameters:

• goal (string) - Description of what this step aims to achieve
• step_index / stepIndex (number, optional) - Explicit step index (auto-increments if omitted)

Returns: string - Generated step ID

assert_() / assert() - Evaluate Assertion

Evaluates an assertion predicate against the current snapshot state. Results are accumulated for the step and emitted as verification events.

# URL assertion
url_ok = runtime.assert_(url_contains("checkout"), "on_checkout_page")

# Element assertion
has_btn = runtime.assert_(exists("role=button text~'Pay'"), "has_pay_button")

Parameters:

• predicate (Predicate) - Predicate function to evaluate
• label (string) - Human-readable label for this assertion
• required (boolean, optional) - If true, gates step success (default: false)

Returns: boolean - True if assertion passed

assert_done() / assertDone() - Assert Task Completion

Asserts task completion with a required assertion. When passed, marks the task as done.

# Check if task goal is achieved
if runtime.assert_done(exists("text~'Order Confirmed'"), "order_placed"):
    print("Order successfully placed!")
    # runtime.is_task_done is now True

Parameters:

• predicate (Predicate) - Predicate function to evaluate
• label (string) - Human-readable label for this assertion

Returns: boolean - True if task is complete

Predicate Helpers

URL Predicates

from predicate import url_matches, url_contains

# Regex match on URL
runtime.assert_(url_matches(r"https://.*\.example\.com"), "is_https")

# Substring match on URL
runtime.assert_(url_contains("checkout"), "on_checkout"

Element Predicates

from predicate import exists, not_exists, element_count

# Element exists (using query syntax)
runtime.assert_(exists("role=button text~'Submit'"), "has_submit")

# Element does not exist
runtime.assert_(not_exists("text~'Error'"),

Combinators

from predicate import all_of, any_of

# All conditions must pass
runtime.assert_(
    all_of(
        url_contains("checkout"),
        exists("role=button text~'Pay'"),
        not_exists("text~'Error'")
    ),

Custom Predicates

from predicate import custom
from predicate.verification import AssertContext, AssertOutcome

def my_predicate(ctx: AssertContext) -> AssertOutcome:
    # Custom logic using ctx.snapshot and ctx.url
    has_items = len(ctx

Assertion DSL (E, expect) — Quick Overview

For expressive, Jest-like assertions, Predicate also ships an Assertion DSL (E, expect, dominant-list queries). DSL expressions compile to predicates — you still pass them into assert/assert_ (or into .check(...).eventually()).

For the full DSL guide and examples, see: Jest‑Style Assertions.

from predicate.asserts import E, expect, in_dominant_list

runtime.assert_(
    expect(E(role="button", text_contains="Submit")).to_be_visible(),
    label="submit_visible"

Assertion Status Methods

Check Assertion Results

# Check if all assertions in current step passed
if runtime.all_assertions_passed():
    print("All assertions passed!")

# Check if all required assertions passed
if runtime.required_assertions_passed():
    print("All required assertions passed!")

Get Assertions for Step End

Retrieve accumulated assertions for inclusion in trace step_end events:

# Get assertions data for step_end event
assertions_data = runtime.get_assertions_for_step_end()
print(f"Assertions: {assertions_data['assertions']}")
print(f"Task done: {assertions_data.

Reset Task State

For multi-task runs, reset the task completion state:

# Reset task_done state for next task
runtime.reset_task_done()

Trace Integration

Assertions are automatically emitted as verification events to the tracer, making them visible in Studio timeline.

Verification Event Schema

{
"type": "verification",
"data": {
  "kind": "assert",
  "label": "on_checkout_page",
  "passed": true,
  "required": false,
  "reason": "URL contains 'checkout'",
  "details": { "url": "https://example.com/checkout" }
},
"step_id": "abc-123"
}

Task Done Event

When assert_done() passes:

{
"type": "verification",
"data": {
  "kind": "task_done",
  "label": "order_placed",
  "passed": true
},
"step_id": "abc-123"
}

Complete Example

from predicate import (
    AgentRuntime,
    PredicateBrowser,
    all_of,
    exists,
    not_exists,
    url_contains,
    url_matches,
)
from predicate.tracer_factory import create_tracer

def

API Reference

Predicate Functions

AssertOutcome

AssertContext

FAQ: Jest/Playwright already has toBeEnabled() — what's different?

What Predicate adds:

• Semantic element identity — Choose the right element via ordinality, grouping, and semantic matching (not brittle CSS/XPath)
• Stability-aware verification — Wait for the page to settle (snapshot_confidence) before asserting
• Explainable failures — Reason codes (element_not_found, dom_unstable) + nearest-match suggestions
• Bounded retries + escalation — .eventually() with deterministic backoff; optional vision fallback only when structure fails
• Works inside Jest — Predicate can run under Jest as the harness; it doesn't replace Jest

Mapping to Jest/Playwright Matchers

Jest/Playwright matchers like expect(locator).toBeEnabled() check state at a point in time, given a locator you already have.

Predicate assertions like runtime.assert_(is_enabled(selector)) are stability-aware and include element selection semantics + retry logic.

Conceptual mapping:

• Predicate is_enabled(...) ↔ Jest toBeEnabled() — but Predicate finds the element semantically first
• Predicate is_checked(...) ↔ Jest toBeChecked()
• Predicate text_contains(...) ↔ Jest toContainText()
• Predicate exists(...) ↔ Jest toBeVisible() / toBeAttached() — but with snapshot context
• Predicate .eventually(...) ↔ Jest waitFor / retry loops — but Predicate retries with confidence gating
• Predicate nearest matches + reason codes ↔ Jest doesn't provide this by default

Note: Predicate can be used under Jest as the test runner. Jest is the harness; Predicate is the perception + verification layer.

Related: AI-Driven QA for Enterprises

If you're interested in using Predicate assertions for enterprise QA workflows — pre-release validation, regression testing, and monitoring critical user flows — see AI-Driven QA with Predicate.