From Agentic Engineering becoming the new buzzword in boardrooms to AI agents quietly writing prescriptions and designing construction schedules, this edition of Konversation zooms in on what truly matters right now: cheaper inference changing the AI business case, and industrial AI that’s finally moving from pilots to production in major industries.
Top 3 Market Insights:
- Monorepos in AI: A monorepo is a single repository that contains applications, services, and shared libraries managed together. In a few eCommerce and retail companies, we see tech teams are adopting monorepos where web, mobile, and admin dashboards share product, catalog, payment modules, and design systems. It promotes frontend/backend coordination, pricing/promo updates, and cross-channel feature rollouts.
- Why Inference Cost Wars Matter for Your AI Budget: New ultra-efficient models force legacy providers to cut prices by 10-50x. Orgs with flexible AI architectures switch to cheaper providers in days while those tied to single vendors stay locked in to pay premium prices. We always consult clients to build architectures which enables them to control vendor decisions- not the other way around.
- AI Configuration Errors, Now Board-Level Risks: As AI controls power grids and industrial systems, small errors can lead to physical damage and prolonged outages. Organizations deploying operational AI without human in the loop face existential risk from their own systems. In one of our recent discussions in the utility industry, we observed board-level involvement in specific areas of AI implementation.
Konverge.AI – Under the Hood
Our Work Highlights:
- Heard of Agentic Engineering? Let’s talk about building a “Jarvis” for your org., Interested?After the launch of Clawdbot, Agentic Engineering systems for enterprises has been in talks. These are basically supervisor agents collaborating with specific task-based agents. This means productivity gains, but a few executives have highlighted security concerns in a few cases. To know more, talk to our CTO Ketan.
- No Code Pipeline using AI in Palantir: Usually data pipelines in Palantir were made using drag-and-drop, node-based creation, but with the recent upgrade orgs can generate entire pipeline using an AI Copilot that generates nodes from natural language prompts. This was useful for one of our project teams in an ongoing client implementation.
- Multi-Agent Framework for a Construction giant: In a POV for a construction client, we are using LangGraph to orchestrate multiple specialized agents, each designed for specific data sources and workflows. These are individual agents dedicated to specific data endpoints with a supervisor agent managing the entire workflow. This approach eliminates traditional data silos. Want to know more about this? Let’s talk.
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