AI Agent Framework & Enablement

Created a repeatable framework for designing, deploying, and scaling AI agents across teams and tools.

Context

As interest in AI grew across Product, Design, and Research, teams were eager to experiment—but there was no shared approach for how AI agents should be designed, built, or adopted. Early efforts risked becoming fragmented, one-off solutions that were difficult to scale or support.

The organization needed a way to move from AI curiosity to AI capability.

Problem

  • No consistent model for designing or deploying AI agents

  • AI efforts varied widely by team and tool

  • High risk of duplication, rework, and low adoption

  • Limited guidance on enablement, rollout, and long-term ownership

Without structure, AI would remain experimental rather than operational.

My Role

Design Program Manager / Design Operations Lead (AI Enablement)

I defined the strategic and operational foundation for AI agents—creating a repeatable framework, aligning cross-functional partners, and ensuring agents could be adopted, supported, and scaled across teams.

What I Built

AI Agent Framework (8 Phases)

I created an end-to-end framework that standardized how AI agents moved from idea to adoption. The framework covered the full lifecycle, including:

  • Discovery & planning

  • Use-case definition

  • Knowledge sourcing

  • Agent configuration

  • Enablement & rollout

  • Iteration and governance

This model provided teams with clarity, consistency, and confidence when building agents.

AI Agent Framework: From Discovery to Deployment

Reusable Agent Design Patterns

Using the framework, I established repeatable patterns that could be applied across tools and teams—reducing friction and accelerating delivery. These patterns focused on:

  • Clear user intent

  • Structured inputs and outputs

  • Defined success criteria

Rather than reinventing solutions, teams could build on proven foundations.

Reusable agent patterns that reduced rework and accelerated delivery

To ensure agents were actually used, I paired technical work with an enablement strategy:

  • Training and onboarding guidance

  • Use-case documentation templates

  • Ongoing support and iteration loops

This shifted AI from “something new to try” into something teams could rely on.

Enablement & Adoption Model

Cross-Tool Application

The framework was intentionally tool-agnostic and successfully applied across multiple platforms, including:

  • Product analytics and research tools

  • Design system documentation

  • Knowledge and content systems

This demonstrated that the approach scaled beyond a single use case or vendor.

Standardized patterns for intent, inputs, outputs, and success criteria

Impact

Established a shared, repeatable approach to AI agents

  • Reduced fragmentation and duplication across AI efforts

  • Enabled teams to confidently design and deploy agents

  • Positioned AI as an operational capability rather than an experiment

Most importantly, teams could focus on solving real problems, not figuring out where to start.

What I’d Do Next

With additional time, I would:

  • Integrate deeper automation between agents and core systems

  • Introduce more usage and performance signals

  • Expand enablement beyond Design into broader product team

Tools & Methods

AI enablement · Program frameworks · Agent lifecycle design · Cross-functional alignment · Knowledge systems · Governance models · Change management