Practices for Integrating AI Agents into Enterprise Systems 【Agent Hub / Experience Topology】 💡 Catchy Message "The #1# reason AI agents go unused after deployment? Users don't know they exist or can't figure out which one to use." Even the best agent delivers zero value if it doesn't reach users. Choosing the right experience topology -- hub vs. embedded -- determines the ROI of your AI investment. 🔥 Problems Solved - "Which tool or agent should I use?" discoverability problem - Context-switching overhead for cross-system workflows - Low adoption rates from scattered AI capabilities across multiple apps - Cost of rebuilding permission models for embedded agents (solved by reusing existing app auth) 🏗️ The Pattern The Hub model provides a single AI entry point (Slack bot, web portal) that routes all requests. Users describe tasks in natural language, and intent classification delegates to the right domain agent (sales, IT, HR, etc.). The Embedded model (copilot) places agents inside existing app UIs (Salesforce side panel, Slack bot, in-app widget), leveraging on-screen context for high-accuracy suggestions with zero context switching. In practice, most organizations combine both: the hub serves as the front door for company-wide AI access, while high-dwell-time apps get dedicated embedded agents -- all sharing the same orchestration layer underneath. ✅ When to Adopt - Hub: Cross-system workflows are common. Tool discoverability is a problem. A single entry point adds value. - Embedded: Work completes within one system. Users spend extended time on that screen (sales, support, dev). - In practice, combining both is most effective. ⚠️ Pitfalls - Poor intent classification in the hub sends users on wild goose chases, destroying trust. Design for clarification questions before delegation. - Blanket-deploying embedded agents across all apps wastes investment on low-dwell-time screens. Prioritize high-engagement apps first. - Building separate orchestration layers for hub and embedded creates duplicate investment and quality inconsistency. 🛠️ Implementation Approach - Build the hub entry point using Slack Bolt or a Microsoft Teams app. Deploy as the company-wide single AI bot where users submit tasks in natural language. - Implement an intent classification engine (P16 Supervisor/Router). Parse user input and delegate to the appropriate domain agent (sales, IT, HR, dev, etc.), with clarification prompts for ambiguous requests. - Deploy embedded copilots starting with highest-dwell-time apps. Build a Salesforce LWC (Lightning Web Components) side panel copilot and a Slack in-channel assistant, passing on-screen context to the agent. - Implement token exchange (P08 OAuth Token Exchange / OBO) so both hub and embedded paths propagate user permissions to backend systems. After SSO via corporate IdP, all SaaS operations execute under the actual user's authority. - Share a common orchestration layer between hub and embedded. Centralize domain agent logic in one place and absorb frontend differences (Slack, Salesforce, Web, etc.) through an adapter layer. #AIAgents# #EnterpriseArchitecture#

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Damn vulnerable Simplicity covenants #3#. Use `jet::sig_all_hash()` to construct transaction topology messages properly. Otherwise, it's fairly easy to allow signature replay attacks.

Damn vulnerable Simplicity covenants #2#. Make sure that an attacker can't alter the transaction topology to bypass the assertions. In this example, checking that the Vault always sits in the "zero input" is critical.

Announced today at #MSBuild#: Microsoft unveiled Majorana 2, a next-generation topological quantum chip developed with the help of Microsoft Discovery’s agentic AI.

Japan’s Coast Guard Hydrographic and Oceanographic Department has released a free Minecraft map built from real submarine topographic data. Players can explore the seabed from Tokyo Bay to the Mariana Trench, including detailed areas such as the Izu Islands and Iwojima ridge. The terrain is scaled for better navigation, and the map includes a coordinate lookup tool that generates teleport commands to specific real-world locations. It is available for both Java and Bedrock editions and focuses on exploration rather than survival gameplay.