Northstars
Every interaction your agents handle generates data. Here's how it gets captured, structured, and put to work.
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What is agentic data?
Every interaction your agents handle generates data that human workers never produced - not just what was said, but what was looked up, what was decided, what happened at every step of execution. Agentic data turns those agent actions into structured, queryable intelligence, defined and written automatically during execution, with no post-processing or manual review required.
Structured, connected data since extraction
Beyond standard classifications like outcome, intent, and sentiment, workflows can define and capture any variable specific to your operation like a quoted price, a document reference number, a compliance acknowledgment, and more. Any value that matters to your operation can be captured and made available downstream.
Different workflows capture different things. An inbound support workflow extracts resolution status and escalation reason. An outbound follow-up workflow extracts response, objection type, and next action. Each workflow defines its own extraction rules, and all output lands in the same data layer, structured consistently so data across workflows and agents is comparable.
Every data point captured during an interaction is automatically mapped to the entity it belongs to, such as a contact, an account, a vendor, a product. The data layer builds a structured understanding of your operation's key entities over time rather than accumulating unattributed records. A call about a shipment adds to both the contact record and the shipment record simultaneously. These relationships allow agents to reason across your entire operation, reducing the chaos due to data siloes and enabling organization-wide cross-entity automation.
Interaction data to actionable record
Memory snippets are structured summaries of past interactions, like what was actually discussed, resolved, or left open. They are written at the end of each session and indexed for retrieval. When the same contact returns, the agent surfaces relevant snippets for that context, such as a prior complaint, an open question, a preference expressed, without retrieving the entire interaction history.
Behavioral tags are derived from patterns across multiple interactions. An escalation history, a recurring contact reason, a consistent sentiment, and more - these patterns are applied as typed tags to the contact record. Agents use behavioral tags to adjust handling before a conversation begins, such as recognizing a contact with a history of escalations, or one who has called about the same issue multiple times, and routing or responding accordingly.
Phone number and email are the primary identity anchors, matching a caller to their existing record before the first word is spoken. Where neither is available, custom identifiers like a tracking number, an account ID, an order reference, etc. are used so interactions can still be mapped to the right contact. When a contact reaches out across different channels, the resolution layer connects those touch points to a single record over time.
Agents query Context mid-workflow
Agents can query the data layer during a live session, retrieving a value captured in a prior interaction, checking whether a condition has been met before, looking up what was discussed last time a contact called. Agents never treat returning customers as strangers.
Data written to Context can trigger downstream workflows automatically - a captured escalation reason routing to a follow-up agent, a classified outcome initiating a CRM update, a detected sentiment threshold triggering a retention workflow. Context is an active data layer, so what gets captured during one interaction can set the next one in motion.
Data retention, access, and how it flows out of the platform
Configurable retention
Data retention periods are configurable per deployment. Transcripts, extracted fields, contact records, and session metadata can be retained for defined windows aligned to your regulatory and operational requirements and removed automatically when the retention period expires without manual intervention.
Tenant isolation
Every deployment's data is fully isolated. No data is shared across tenants, used to train shared models, or repurposed for any purpose outside the execution context it was captured in. What your agents capture stays within your deployment
Portability via REST API
The full data layer is accessible through the REST API and managed REST gateway, and is queryable by external BI tools, data lakes, analytics platforms, and operational dashboards. The data your agents generate is treated as a first-class organizational data source, exportable and integrable with your existing stack on your terms.
Agentic tools are what allow agents to complete tasks rather than just conduct conversations. Click below to learn more about how HappyRobot agents are built and deployed.