Adaline Agent Improvement Platform

Adaline, Inc. builds an agent improvement platform that helps AI teams improve deployed agents autonomously. Its privacy policy lists a Delaware address; the founding year is not stated on the pages fetched.

The platform ingests production traces, groups recurring agent patterns into behaviour maps, and generates evaluations from detected behaviours and user feedback to catch regressions. It also creates synthetic test data for edge cases and acts as a feedback loop between traces and agent changes. Logos on the site include Salesforce, DoorDash, HubSpot and McKinsey & Company.

• Trace ingestion: collects production traces from deployed agents.
• Behaviour maps: groups recurring agent patterns.
• Auto evals: generates evaluations from behaviours and user feedback.
• Synthetic data: creates edge-case test data.
• Feedback loop: links trace findings to agent changes.
• Enterprise controls: SSO/SAML and PII data processing agreements.

A Vietnamese fintech can detect where its support agent fails on refund questions. A retailer can turn customer feedback into regression tests for a shopping assistant. A BPO firm can monitor several client agents for quality drift.

Evaluations generated from real traces reduce manual test writing. Behaviour maps show where an agent fails most often. Synthetic edge-case data widens coverage before release. SOC 2 Type II, GDPR, HIPAA and SSO/SAML support enterprise procurement.

Deployed agents degrade as data and user behaviour shift, so continuous improvement loops are replacing one-off testing. Evaluation generated from production traffic is becoming a standard practice. Vietnamese teams running customer-facing agents will need the same discipline.

Legal entity Adaline, Inc., Wilmington, Delaware address per privacy policy. Lists SOC 2 Type II, GDPR, HIPAA and SSO/SAML. Open-source repositories under the adaline GitHub account.