mParticle

Drive the product through a documented public API G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Connect an agent via an official MCP server G

Agent access

Plug MCP servers into this product so it can use their tools G

Agent access

Build against official SDKs G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Run the product headlessly / in CI for automation G

Agent access

Set up automations that run autonomously in the background G

Agentic features

Operate the product with natural-language commands G

Agentic features

Issue scoped/least-privilege API credentials for an agent G

Agent access

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Point an agent at llms.txt or agent-oriented docs G

Agent access

Rely on versioned APIs with a documented deprecation policy G

Api quality

Subscribe to events via webhooks G

Agent access

Use an official CLI G

Agent access

Test against a sandbox environment without touching production data G

Api quality

Anonymous and known activity stitches into one customer profile across devices, with documented and configurable identity-resolution rules C

Stitching

User consent is captured and enforced across destinations — opt-outs and consent categories are honored downstream automatically C

Consent

Collect events from web, mobile, and server apps through official SDKs that implement a documented tracking spec (track, identify, page) C

Sdks

Route events to a large catalog of documented destination integrations with per-destination mapping and filtering C

Destinations

Define rules that trigger actions automatically on events G

An agent can manage the pipeline itself — create sources and destinations, wire streams, inspect deliveries — through a documented API or MCP server C

Agent pipeline

An agent can query customer data and create or activate audiences end to end through documented APIs or MCP — no dashboard in the loop C

Agent audiences

Raw events and profiles land in my own warehouse or lake (Snowflake, BigQuery, ClickHouse, S3) on a schedule I control C

Warehouse sync

Build audiences from traits and behavior in a visual builder — no SQL required — and see estimated size before activating C

Audiences

Export all of my data in open formats and leave G

Prevent my data from being used to train AI models G

Self-host the core product G

Audiences sync to ad platforms and engagement tools continuously, with membership entering and exiting in near-real-time C

Activation

Process user deletion and suppression requests (GDPR/CCPA) and have them forwarded to connected destinations C

Deletion

Query unified customer profiles — traits, identifiers, event history — through a documented profile API or store C

Profiles

Send events server-to-server through a documented HTTP ingestion API with authentication and delivery guarantees C

Ingest

Computed traits and predictive scores (LTV, churn or purchase propensity) are calculated on profiles and usable in targeting C

Traits

Control PII flow per destination — hashing, masking, and field-level filtering of sensitive attributes C

Pii controls

Describe an audience in natural language and AI builds the segment definition for review, grounded in my actual schema C

Ai assist

Do everything through the API that I can do in the UI G

Perform bulk operations across many items at once G

A tracking plan or schema is enforced — violating events get flagged, blocked, or quarantined instead of silently corrupting downstream data C

Data quality

Control data retention and deletion G

Replay archived events into a new destination or backfill history when a tool is added or a pipeline breaks C

Replay

Run warehouse-native: define models and audiences on tables already in my warehouse and activate them without re-collecting the data (reverse ETL) C

Composable

Transform, filter, and enrich events in-pipeline with custom code or functions before they reach destinations C

Transformations

AI decisioning agents pick messages, timing, and channels per customer autonomously within guardrails I set, with measurable lift C

Ai decisioning

Choose where my data is stored (region/residency) G

Watch events flow live and diagnose delivery failures per destination — debugger views, delivery metrics, and alerting C

Observability

Opt out of telemetry and usage tracking G

Read the product's source under an open license G

Schedule recurring jobs or workflows G

Pull customer data in from third-party cloud apps and feeds — not just my own instrumented apps C

Sources

Version, review, and roll back my automations G