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“Human judgment, context, oversight, knowing the broader picture—let’s keep the human managing those things while the agent becomes a powerful tool to enable that human operator to do their job better, faster, with better intel.”
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tl;dr – what you’ll get:
- Understand why unified data and one AI interface can outperform a fragmented collection of point agents.
- See how model-agnostic AI and “bring your own LLM” architecture reduce vendor lock-in and give commerce teams more control over cost and flexibility.
- Learn how Playbooks automate everyday commerce tasks while preserving human oversight, permissions and accountability.
Summary:
Eric Rosado’s return to Kibo Commerce as Chief Product Officer comes at a pivotal moment for ecommerce technology. After previously serving as Kibo’s VP of Product and spending three years as CPO at Monetate, Eric returned to help shape Kibo’s next phase of product innovation, as AI accelerates software development and the way commerce teams operate.
The vendor market is evolving rapidly. AI is enabling companies to build faster but that creates new challenges: deciding what to build, keeping pace with constant model innovation and helping customers navigate a crowded market where vendors often promise similar capabilities.
Product teams must now design not only for customer experience, but also for “agent experience”, making their platforms easy for AI agents to understand and use.
That thinking is central to Kibo AI, Kibo Commerce’s newly available agentic layer. Rather than exposing customers to a growing collection of individual agents, Kibo AI brings nine specialised agents together behind one unified interface spanning commerce and order management.
Its unified data model and API-first architecture allow agents to work across catalogue, pricing, promotions, inventory, routing, fulfilment and orchestration without forcing users to understand the underlying complexity. Kibo AI is also model agnostic. Through its “bring your own model” architecture, customers can use OpenAI, Anthropic, Gemini or open-weight models according to their existing technology strategy, performance requirements and budget.
Our conversation also explores Playbooks: plain-language automations that run on triggers or schedules. They can monitor new products for missing content, flag fulfilment risks, generate reports and connect with external services such as translation engines or Slack.
It’s important that we emphasise human oversight. AI can analyse, explain and prepare actions, but permissions, audit trails and approval steps remain in place, helping commerce teams automate repetitive work without surrendering control.
Episode chapters:
- [00:30] Introducing Kibo Commerce and the conversation on AI, tooling and agentic commerce.
- [04:25] What Kibo Commerce offers: a unified, composable ecommerce and OMS platform.
- [07:30] How AI is changing the technology vendor market.
- [11:15] Designing for agent experience alongside user experience.
- [18:55] Bring Your Own Model: why Kibo AI is model-agnostic.
- [24:25] Kibo AI Playbooks: automating repeatable ecommerce operations.
- [28:35] Order orchestration, SLA monitoring and scheduled operational reports.
- [33:05] Why explain and analyse agents can be as valuable as configuration agents.
- [35:40] Using AI to understand complex B2B commerce data and account structures.
- [40:55] AI safety: permissions, human approval and audit trails.
- [44:30] Keeping humans in control while reducing repetitive work.
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