In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.

Our ITSM platform never lacked data, but it hasn’t always been easy to understand what the data was telling us and what actions we should take next, despite all the time spent building catalog items, interpreting data and usage licenses, and maintaining CMDB health.

A retrieval-augmented generation (RAG) demo is easy to get excited about.

Most conversations about AI security still orbit the model itself: Is the large language model (LLM) aligned, can it be jailbroken, does it hallucinate under pressure? Those are real questions. But as organizations move AI agents and LLM-powered workflows from pilot projects into production, a different category of risk is emerging, one that has almost nothing to do with the model's weights and everything to do with the system built around it.

The plan asks every capable model to carry a safety certification. Closed models can. Open ones can't. Nobody has to ban anything.

When a microservice fails in a cloud-native architecture, circuit breakers trigger, traffic reroutes, and Kubernetes spins up replacement pods within seconds. The system heals before end users even notice a blip.

Imagine deploying an AI agent to run a multi-day master data validation workflow. By day three, it has ingested thousands of records. The baseline governance rules you hardcoded into the system prompt at the start? They are gone. Pushed right out of active memory.

InMarket's pipeline ran error-free but produced 40% wrong audience counts due to a silent schema change, stressing the importance of semantic checks.