Comprehensive overview of your organization and target business unit for the agent.
Business challenges, purpose, main features and end-users of the agent.
Internal team, product team and the partner implementation teams.
Detailed timeline with key milestones and planned go-live dates.
Various performance metrics and any expected ROI.
Solution Modelling involves designing technical implementation blueprint and architecture, conversation flows, volumes, license capacity, backend actions, data sources, and deployment channels.
Smart adaptive cards, leverage platform’s language understanding & Gen-AI orchestration for dialog management and take actions.
Integrations through connectors: Leverage the highly extensible integration framework of the platform with 1400+ connectors and custom Direct Line APIs for more complex integrations, to build powerful AI agent.
Leverage Azure services for customized deployment channels and handover scenarios like hand-off to a live agent in Dynamics 365 etc.
Leverage Copilot Studio’s robust security framework, which includes the following key capabilities:
At the tenant, environment, and agent levels to enforce critical security controls such as authentication requirements, channel restrictions, and limitations on knowledge sources and connectors.
Manage all access permissions through security groups.
Address client-specific regulatory requirements—such as GDPR, data residency, and data retention policies.
Deeper insights are needed advanced performance measurement against target KPIs and continuous optimization and reliability of AI agent operations.
Integrate Application Insights to capture detailed telemetry such as user activities, errors, custom events, and generative answer metrics.
Leverage in-built CoE Starter Kit and Copilot Studio Starter Kit for
Monitoring licensing, consumption. and Capacity.
For source control and automated deployments using Azure Dev Ops in development, QA and production.
Automating bulk testing.
