Show Notes
Season 2, Episode 1 - Agent Memory Is More Than RAG
AI agents can access huge amounts of enterprise data, yet they often forget the context that matters.
In this episode of Agent Sense, Monika Aggarwal and Frank Chavez talk with Ran Aroussi, founder and lead architect of MUXI, about what it takes to build useful agent memory. We discuss why storing data and using RAG does not create memory, what agents should remember, how memory can be distilled and updated over time, and why enterprise memory needs context, relationships, time, and source traceability. Ran also shares his perspective on building memory as an organizational capability, with access controls and governance built into the architecture. A practical conversation for architects, engineers, and AI leaders building agents for production. Guest: Ran AroussiFounder and Lead Architect, MUXICreator of yfinance Agent Sense is hosted by Monika Aggarwal and Frank Chavez. #AgentSense #AIAgents #AgentMemory #RAG #EnterpriseAI #MUXI
More from Agent Sense | Agentic Workflows & Operational AI
Your AI agent works, but can you prove its business value? As enterprises invest in Agentic AI, AI automation, and autonomous AIagents, many struggle to demonstrate measurable ROI. The challenges oftenbegin before development, with unclear business requirements and poorlyunderstood workflows. In this episode, hosts Monika Aggarwal and Frank Chavez speak withRobert Maxwell, Founder and CEO of TGT Solutions, who brings over 30years of enterprise technology experience. Robert shares practical insights on AI agent implementation, businessprocess automation, production readiness, AI governance, and measuring AI ROI.We discuss why successful demos do not always translate into business value andwhat enterprises need to address before scaling AI agents. The key question: How do you move from building AI agents to delivering measurablebusiness outcomes? 🎙️ Agent Sense: Practical conversations about Enterprise AI. 🌐 agentsensepodcast.com #AgenticAI #AIAgents #EnterpriseAI #AIROI #AIAutomation
𝐀𝐠𝐞𝐧𝐭 𝐒𝐞𝐧𝐬𝐞 𝐢𝐬 𝐚 𝐬𝐡𝐨𝐫𝐭 𝐩𝐨𝐝𝐜𝐚𝐬𝐭 𝐨𝐧 𝐦𝐚𝐤𝐢𝐧𝐠 𝐀𝐈 𝐬𝐢𝐦𝐩𝐥𝐞, 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥, 𝐚𝐧𝐝 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝 An AI agent can be 95% accurate and still fail thebusiness. It can give the right answer and still take too long, use the wrong tool, cost too much, or fail to complete the task. Frank Chávez and I talk with Jay Migliaccio, TechnicalProduct Manager for IBM watsonx Orchestrate, about how enterprises shouldmeasure and improve agent performance. Jay Migliaccio has spent his career in product and salesroles across early-stage software companies, and is now building agentic solutions for enterprise customers. That builder's view shapes everything below. Themes of the episode: 🔹 Why accuracy alone hides agent failures 🔹 How to measure outcomes, quality, operations,and user experience together 🔹 How observability traces a failure to the model,instructions, tools, workflow, data, or integration — IBM watsonx Orchestrateshipped a full observability dashboard this year for exactly this 🔹 Why teams need measurable goals and aperformance baseline before launch Jay's diagnostic approach: start with the businessoutcome, trace the full execution, find the real failure point before touchingthe model or the prompt. The Agent Performance principle: 𝑴𝒆𝒂𝒔𝒖𝒓𝒆𝒕𝒉𝒆 𝒐𝒖𝒕𝒄𝒐𝒎𝒆→ 𝑻𝒓𝒂𝒄𝒆 𝒕𝒉𝒆 𝒆𝒙𝒆𝒄𝒖𝒕𝒊𝒐𝒏→ 𝑭𝒊𝒏𝒅 𝒕𝒉𝒆 𝒇𝒂𝒊𝒍𝒖𝒓𝒆𝒑𝒐𝒊𝒏𝒕 → 𝑰𝒎𝒑𝒓𝒐𝒗𝒆𝒂𝒈𝒂𝒊𝒏𝒔𝒕 𝒂 𝒃𝒂𝒔𝒆𝒍𝒊𝒏𝒆 Agent Sense web page and all episodes: 🔗agentsensepodcast.com #AgentSense #EnterpriseAI #AgentObservability ⚠️ Disclaimer: The views shared are based on our personalexperience and do not represent the views of IBM.
Most AI agents wait for a person to open a chat and give them a prompt. Ambient agents work differently. They listen for events across enterprise systems and start working when something changes. In this episode of Agent Sense, Monika Aggarwal and Frank Chavez discuss what makes an agent ambient and how the architecture changes when AI works continuously in the background. Using an SRE Ambient Agent as a mature example, they discuss how an agent can monitor operational signals, investigate incidents, invoke other agents and tools, involve a human when needed, and verify the outcome. The Ambient Agent design principle: Start with an event → Bound the action → Earn autonomy.
AI can summarize a meeting. Enterprise work starts after the summary. In this episode, Monika Aggarwal speaks with Artem Koren, co-founder and Chief Product Officer of Sembly AI, about how meeting context can move beyond notes and trigger real work. They discuss how AI can support follow-ups, workflow updates, CRM actions, and executive reporting across teams, countries, languages, and meeting platforms. Artem also explains why humans must steer agent execution and why enterprises should start with a controlled process, clear success measures, and a specific business outcome. Agent Sense keeps enterprise AI simple, practical, and grounded. Agent Sense reflects the personal perspectives and experiences of its hosts and guests. It is not an official IBM podcast.
Agent Sense — Episode 8: Governed Catalog of AI Assets Enterprise AI agents does not fail from lack of ideas. They fail from agent sprawl: duplicate builds, unclear ownership, no way to know what is safe to reuse. Jyotsna Narayanan, Principal Product Manager for watsonx Orchestrate at IBM, joins host Monika Aggarwal to break down what a governed catalog of AI agents, tools, and MCP servers needs to include, how to evaluate reuse readiness using references like OWASP Top 10 and CoSAI, and where to start. Topics: AI agents, agentic AI, enterprise AI governance, AgentOps, MCP, Model Context Protocol, watsonx Orchestrate, agent catalog, AI reuse, enterprise automation, production AI. Vendor-agnostic, not monetized, based on field experience. Views are personal and do not represent IBM.