Continuous Agent Quality

Your agents break silently.
BenchForge catches it.

Automated quality baselines that run continuously against your AI agents. Define scenarios once, detect regression instantly, ship with confidence.

benchforge run --cohort production
1// Define a baseline scenario
2scenario "onboarding-flow" {
3  agent: "chat-agent-v3",
4  input: "New user, no context",
5  expect: {
6    completes_research: true,
7    sends_email: true,
8    creates_tasks: >= 3,
9    quality_score: >= 0.85
10  }
11}
12
13// ✓ 47 scenarios passed | 2 regressed | 1 new baseline

Quality you can measure

Every run produces structured scores. Track drift across deploys, models, and prompt changes.

47/50
Scenarios passing
-2.1%
Score delta (7d)
142ms
Avg eval latency

How it works

01

Define scenarios

Write scenario files that describe what your agent should do, what inputs it receives, and what outcomes you expect. Version them alongside your code.

02

Run cohorts

Execute scenarios against live agents in isolated environments. Each run produces structured scores, traces, and behavioral snapshots.

03

Detect regression

Compare scores against established baselines. When quality drops below threshold, you know before your users do. Flag prompt changes, model updates, or code deploys that caused it.

04

Promote baselines

When a run meets or exceeds quality thresholds, promote it as the new baseline. The bar only moves up.

Not another eval platform

Capability Generic Eval Tools BenchForge
Automated recurring runs Manual trigger Continuous
Cohort-based scenarios No Built-in
Baseline promotion No One command
Regression detection Side-by-side Automatic alerts
Multi-agent scoring Per-prompt End-to-end

Ship agents that stay good.

Quality isn't a launch metric. It's an ongoing contract. BenchForge makes sure you keep it.