Klaviyo adds Custom Skills to expand its Customer Agent
Klaviyo’s Custom Skills adds brand-specific logic and integrations to its Customer Agent, reflecting the shift toward CRM-native AI service.
Klaviyo has released Custom Skills for Customer Agent, letting brands extend its AI service agent with business-specific logic, custom behaviors, and connections to external systems. The update is available in managed beta and is positioned as a way to move beyond fixed, out-of-the-box agent capabilities.
For marketers and CX teams, the change matters because it pushes AI agents closer to being configurable workflow layers that sit on top of customer data, rather than simple chat interfaces that answer FAQs.
Table of contents
Jump to each section:
- What Custom Skills changes in Klaviyo’s Customer Agent
- Why CRM-native agents are becoming a platform strategy
- Competitive landscape for AI service and engagement platforms
- What to evaluate before rolling out custom agent logic
What Custom Skills changes in Klaviyo’s Customer Agent
Customer Agent already supports common commerce interactions such as order tracking, returns, and product recommendations. Custom Skills extends this by allowing brands to define logic in plain language, create skills from scratch, adjust how existing skills behave, and connect the agent to systems across their tech stack.
Klaviyo frames the need around operational differences between brands, such as returns policies for luxury versus mass retail, or loyalty mechanics for subscriptions versus one-time purchases. The point is less “more AI” and more “more control,” so the agent can reflect how the business actually operates.
Examples Klaviyo highlights include reservation concierge flows, gifting advisors that do not contaminate the shopper’s own profile, and integrations that connect to physical-world systems like store locators, appointment booking, or warranty claims.
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Why CRM-native agents are becoming a platform strategy
Klaviyo’s central architectural argument is that service agents perform better when they are built on the same customer profile system that powers marketing. In this model, conversations can use real-time context (purchase history, browsing behavior, loyalty status), and outcomes can be written back into the profile to influence future segmentation and personalization.
This fits a broader trend toward marketing and service convergence: brands want consistent customer context across acquisition, retention, and support, and they want automation to act on that context without data syncing delays. AI agents also introduce a new “experience surface” where the quality of the underlying data model can determine whether responses are merely plausible or operationally correct.
For teams, the strategic question becomes governance: if the agent can execute business logic and update profiles, then configuration, permissions, and testing start to look more like software release management than campaign management.
Competitive landscape for AI service and engagement platforms
Klaviyo competes in the B2C CRM and customer engagement market with vendors such as Braze, HubSpot, Salesforce, and Omnisend. The competitive intensity is high because these platforms increasingly overlap across data, messaging, automation, and now service experiences.
Many AI service tools historically evolved from helpdesks, where tickets are the system of record. Klaviyo is pushing a different center of gravity: the CRM profile as the system of record, with the agent operating where marketing context already exists. Salesforce and HubSpot can make similar claims through their CRM foundations, while Braze tends to be evaluated as a customer engagement platform with deep orchestration and data integrations.
Custom Skills, as described, is a bid to differentiate on extensibility: brands can tailor the agent and connect it to external systems without waiting for product roadmap coverage. The real competitive test will be how safely and predictably teams can implement custom logic at scale, and how much effort it takes to maintain those skills as policies and catalogs change.
What to evaluate before rolling out custom agent logic
Custom agent logic introduces new failure modes beyond incorrect answers. Teams should start with bounded workflows (returns eligibility, order changes, appointment booking) and define explicit escalation paths when the agent is uncertain or when a request has financial or policy implications.
Data quality and identity resolution become prerequisites. If the CRM profile is incomplete or contains mismatched identities across channels, personalization can degrade quickly and create support risk. Brands should also verify what gets written back into the profile after a conversation, and which fields should be protected from automated updates.
Finally, because Custom Skills can connect to other systems, teams should treat integrations as part of their security posture: least-privilege access, logging, and testing against edge cases like partial refunds, split shipments, and multi-address orders.
