Handshake brings professional expertise into AI training and agent evaluation

Many people know Handshake as a career platform. Its AI business applies a related asset—access to people with professional knowledge—to a different problem: teaching and evaluating AI systems that must do useful work. The company now combines paid contributor projects with enterprise AI services and research on how to grade agent behavior. Readers need to distinguish these routes carefully. Joining a fellowship, buying a data program and commissioning an enterprise agent are different transactions.

01 / ProductFrom a career network to an AI data and engineering business

The company overview describes a career network connecting job seekers, educational institutions and employers. The Handshake AI program offers paid work contributing to model training and improvement. That creates a channel for professional knowledge to enter AI development, but the size of a career network should not be mistaken for the number of experts available for a particular project or specialty.

The live Enterprise AI page presents three related jobs: build new agents, improve agent performance and cost, and train models for greater control. It says the team works inside customer environments and emphasizes preserving customer intellectual property. Those are service propositions that require an agreed scope. They do not describe a universally available self-service API with a public unit price.

Handshake also publishes technical work about evaluation. Its Gandalf research describes an agent-based verifier that inspects artifacts and tool state inside a rollout environment. The relevant idea is that checking a finished task can require more than reading the assistant's last message. This provides a concrete research direction behind the enterprise pitch, while leaving the performance of any customer's workflow to be tested independently.

02 / AudienceA fit when professional judgment is the missing input

Imagine an internal agent that prepares account-review materials from several business systems. A polished slide deck can still contain inconsistent figures or miss a crucial qualification. Evaluating that work requires someone who understands the source records and the purpose of the review. Handshake is relevant when the organization needs expert tasks, rubrics and judgments, together with engineering work that turns those judgments into a repeatable evaluation process.

Mercor offers a useful comparison for expert participation in AI work, while Scale AI provides context for managed data and evaluation programs. Handshake's broader enterprise offer should also be compared with the customer's internal engineering capacity. A team that already has clear tests and accessible specialists may need a small data engagement rather than a partner building the entire agent.

03 / WorkflowA proposed evaluation for an account-review assistant

This is an editorial pilot design, not a Handshake customer result. Choose one bounded task: create a draft review from approved source records, without sending it to a client. Give the agent a controlled workspace containing the relevant tables, definitions and prior-period figures. The expected output includes a working artifact, not just a narrative claim that the task was completed.

Ask domain experts to define the acceptance rubric before running candidate systems. Separate factual requirements from presentation preferences. A total must reconcile to its underlying records; a chart must use the correct time period; a caveat must survive the summary. Aesthetic choices may allow several acceptable answers. This separation prevents a fluent presentation from receiving a high score while violating a consequential business rule.

Use the artifact-aware verification idea to decide what evidence the evaluator needs. The evaluator should open the output, inspect relevant formulas or values and trace selected claims to their sources. Add expert review where a rule cannot be checked mechanically. Preserve the grading evidence and disagreement record so a later model update can be compared against the same task definition.

Then test the test itself. Include deliberately flawed outputs with stale values, plausible but unsupported conclusions and missing caveats. If the evaluator accepts them, improving the agent against that evaluator may reward the wrong behavior. Keep an independently reviewed holdout set and inspect false accepts as well as false rejections. Only after the evaluation is credible should the team consider fine-tuning or switching models to improve cost and quality.

04 / PricingKeep buyer costs separate from contributor compensation

The current enterprise page offers a scoping session and does not publish a numeric customer tariff for agent development, evaluation or model training. The AI participation page advertises opportunities for people to earn from projects. Those earnings describe the contributor side of the business and cannot be converted into a buyer price without knowing the management, engineering, review and delivery scope.

For an enterprise proposal, ask which objects the customer receives: task specifications, accepted examples, evaluator code, review explanations and any adapted model artifacts. Clarify whether engineering work, compute and model usage are included or billed separately. A lower inference bill is useful only if the total operating cost remains lower after evaluation, human review and maintenance are accounted for. Public case-study outcomes should inform questions, not become assumed savings in a budget.

05 / DistinctionsExpert work and evaluation research can reinforce each other

Handshake's essay on human expertise explains its view that professional judgment remains important in model training. The practical value lies in encoding what experts notice: missing context, invalid assumptions and distinctions a general rater may overlook. A useful dataset preserves those reasons rather than reducing every judgment to a binary preference. That makes the output easier to audit and more useful when the engineering team needs to diagnose a recurring failure.

The research on verifiers creates a second distinction. An agent that produces a spreadsheet, document or code change must be evaluated through those artifacts and their effects. This is adjacent to LangChain's development and evaluation ecosystem, but Handshake's enterprise pitch also includes expert data and implementation work. The comparison should therefore separate the evaluation tool from the people and engineering required to make the evaluation credible.

This combined approach is most useful when the task has a real professional standard but cannot be reduced to a simple exact-match answer. It is less useful when the organization cannot yet describe what a successful output would do. External expertise can improve a rubric, but business owners must still decide which actions are allowed, which errors are unacceptable and when a person remains responsible for approval.

06 / QuestionsEligibility and evidence need a careful reading

For contributors, Handshake's current work-authorization guidance states that international participation is invite-only and that US participants need supported work authorization and an SSN or ITIN. It describes the program as a contractor model and lists unsupported categories. Prospective contributors should consult that current guidance and their own appropriate advisers rather than treating a generic sign-up button as confirmation of eligibility or guaranteed work.

For buyers, the key uncertainty is the availability of suitable experts for the actual task. Broad network figures do not establish how many people can review a particular language, jurisdiction or technical specialty within the required schedule. Ask for the qualification and calibration process, and review representative judgments. The project should make it possible to distinguish a shortage of expertise from unclear instructions or a flawed evaluation design.

Model ownership also needs a precise contractual meaning. Owning a fine-tuned artifact does not automatically settle the license of the underlying model, the provenance of training examples or the right to reuse proprietary material. Establish those boundaries before the program starts. Keep customer data, generated artifacts and contributor access limited to the scope required for the task, and test that the evaluation can be reproduced after the initial engagement ends.

07 / DecisionChoose the appropriate route into Handshake AI