Keep pulling the thread on Aishwarya Riganti and Kiriti Bado.
By the end of 2026, there will be a rise in 'background' or 'proactive' agents that understand user workflows to anticipate needs and complete work autonomously.
Multi-modal AI experiences will be a major trend in 2026, moving beyond text-only LLMs to incorporate richer, more human-like conversational signals.
A paper from researchers at UC Berkeley and Databricks, including Matei Zaharia and Ion Stoica, found that approximately 75% of surveyed enterprises cited reliability as their biggest problem with AI.
Security issues like prompt injection will become a huge problem once AI systems go mainstream, but currently most teams are too focused on building to worry about it.
The key to success in the current AI market is not being the first to launch an agent, but building the right data and feedback flywheels to enable continuous improvement.
Replacing a critical enterprise workflow with an AI system typically takes four to six months of work, even for companies with optimal data and infrastructure layers.
An Air Canada chatbot hallucinated a refund policy that was not part of the company's official playbook, and the company was legally required to honor it.
Major model updates, such as the deprecation of one API version for another, can break existing AI systems and require a full recalibration due to the new model's different properties.
Jason Lemkin, founder of SaaStr, replaced his 10-person sales team with 1.2 people and 20 AI agents.
Building AI products is fundamentally different from building traditional software due to the non-deterministic nature of both user inputs via natural language and the probabilistic outputs from LLMs.
Developing agentic AI systems requires managing a trade-off between granting the system more agency to make decisions and relinquishing human control.
The CEO of Rackspace, Gajen, dedicates time from 4 a.m. to 6 a.m. daily to stay current on AI developments.