Beaker, the autonomous AI engineer

Beaker autonomously experiments with your AI agents finding failures, testing fixes, and compounding the improvements that work.

pip install beaker-sdk

Try For Free See how it works

Deployed AcrossSupply ChainHealthcareFinanceLegalCustomer Support

Continual Learning Loop

The fastest way to improve your production agents

Point Beaker at an evaluation metric. It turns production failures into hypotheses, makes changes to your AI agent, runs experiments against your data, and scores candidates on quality, speed, and cost.

/ 01

Analyze

Beaker learns from every agent failure your team investigates, grouping and tracing them to likely causes.

214

failures traced this week

/ 02

Hypothesize

Beaker forms hypotheses about what's causing those failures and identifies changes worth testing.

/ 03

Validate

Beaker runs each candidate change against your data, measuring the effect on quality, speed, and cost.

QUALITY

89.4+10.2

LATENCY

1.41s-0.4s

COST

$25.10-18%

/ 04

Compound

Validated improvements become the new baseline, so each round starts from what Beaker has already learned.

baseline v43

Product

Track every hypothesis from failure to fix

See failure patterns, hypotheses, changes, and metric impact in one log down to the samples behind each decision. When a candidate wins, Beaker opens a pull request for your team to review and ship.

beaker / document-reasoning / experiment exp_7f5a9cf

Experiment logsFailuresHypothesisCandidates

Candidate 4 — ready to ship

Quality

89.4+10.2

Latency

1.41s-0.4s

Cost

$25.10-18%

Hypotheses

H-11validated

Long inputs are being cut off halfway through

Fix tool implementation+8.1

H-12invalidated

The instructions contradict themselves on edge cases

Clean up prompts and skills-1.8

H-13queued

The agent re-reads everything at every step

Compress agent context—

H-14queued

It still follows guidance from an earlier, outdated version of the doc

Re-fetch source before each step—

H-15queued

The easy cases are running through the same heavy model as the hard ones

Route by difficulty to a cheaper model—

H-16queued

The cheaper model repeats mistakes that were already corrected once

Feed prior corrections back as few-shot—

H-17queued

The corrections your team makes never make it back into the agent

Turn accepted edits into training examples—

Show all 41 samples →Selected hypothesis · detail

H-11Long inputs are being cut off halfway through

Hypothesis

The tool the agent reads with stops partway through long inputs, so the agent answers from half the document — and never knows it.

Experimental verdict

Validated — 7 of the 7 failures of this kind are gone, and no case that already worked got worse.

Description of the changes

The reader now walks the whole input in pieces and carries what it already read into the next one.

Sample comparisons

case_10280.42 → 1.00

Stopped at page 6 of a 14-page contract and missed the renewal clause. Now reads all 14 and cites it.

case_11530.67 → 0.92

case_12070.88 → 0.88

Show all 47 samples →

beaker / document-reasoning / experiment exp_7f5a9cf

Candidate 4 — ready to ship

Quality89.4+10.2

Latency1.41s-0.4s

Cost$25.10-18%

id	hypothesis	lever	quality	verdict
H-11	H-11 Long inputs are being cut off halfway throughFix tool implementation	Fix tool implementation	—	queued
H-12	H-12 The instructions contradict themselves on edge casesClean up prompts and skills	Clean up prompts and skills	—	queued
H-13	H-13 The agent re-reads everything at every stepCompress agent context	Compress agent context	—	queued
H-14	H-14 It still follows guidance from an earlier, outdated version of the docRe-fetch source before each step	Re-fetch source before each step	—	queued
H-15	H-15 The easy cases are running through the same heavy model as the hard onesRoute by difficulty to a cheaper model	Route by difficulty to a cheaper model	—	queued
H-16	H-16 The cheaper model repeats mistakes that were already corrected onceFeed prior corrections back as few-shot	Feed prior corrections back as few-shot	—	queued
H-17	H-17 The corrections your team makes never make it back into the agentTurn accepted edits into training examples	Turn accepted edits into training examples	—	queued

Case	Before	After
case_1509	0.62 quality	0.91 quality
case_1533	0.71 quality	0.94 quality
case_1587	0.58 quality	0.88 quality

H-11Long inputs are being cut off halfway through

Hypothesis: The tool the agent reads with stops partway through long inputs, so the agent answers from half the document — and never knows it.
Experimental verdict: Validated — 7 of the 7 failures of this kind are gone, and no case that already worked got worse.
Description of the changes: The reader now walks the whole input in pieces and carries what it already read into the next one.

	case	before		after
	case_1028	0.42	→	1.00
Stopped at page 6 of a 14-page contract and missed the renewal clause. Now reads all 14 and cites it.
	case_1153	0.67	→	0.92
	case_1207	0.88	→	0.88

Show all 47 samples →

CONNECT YOUR AGENT

Point Beaker at code you already have

from beaker import Spec, spec

@spec.register("order_ingestion")

def order_ingestion() -> Spec:

return Spec(

agent=targets.EXTRACTION_AGENT,

data_loader=LabeledOrders(),

run_case=OrderIngestionAgent().run,

scorer=order_acc,

)

Your agent, your data, your definition of good

beaker init sets up the repo, and your coding agent finishes the integration

beaker run --dry-run checks that integration at small scale before a full experiment runs

Experimental Protocols

Worked examples from the field

Proven methodologies from real world enterprise use cases. The problem, the approach, and the measurable results.

Get started
Stop your agent from misreading the same carrier PDF twice.
Correct one shipment status and make the format stick.
Get started
Teach your support agent your refund policy in an afternoon.
Turn a week of agents with the policy wrong into drafts that follow it.
Research
Turn 20 analyst overrides into a rule that survives review.
Score a hypothesis against your own real internal data.

Pricing

Pay for the experiments you run

For small teams and startups

Pay-as-you-go

No base subscription. Pay only for the compute your experiments use.

$0+ usageBeta pricing

$40 in credits to start, no card required
Up to 5 collaborators
A budget cap you set per experiment
Community support

Start for free

For organization that need scale and support

Custom

Custom pricing for custom deployments. Talk to us for a quote.

Let's talk

Everything in Pay-as-you-go, plus:

Unlimited collaborators
SAML/OIDC SSO
Centralized enterprise admin controls
Dedicated account management
Highest priority support
Invoice-based pricing
Dedicated & self-hosted deployment options

Start with the SDK

Ready when you are

Stop babysitting your agents. Start compounding.

Connect your first agent in minutes. Beaker starts finding fixes on day one.

Start free →Book a demo