Melissa Incera is a research analyst on the Data, AI & Analytics team at 451 Research from S&P Global Energy Horizons. Her focus lies in generative AI, its emergent use cases in enterprise applications and the foundation models that underpin it. Specifically, she tracks the text-generation landscape and its offshoots in enterprise search, AI assistants, marketing and writing tools. Before joining the AI team, Melissa spent several years covering technology M&A, analyzing significant deals and forecasting trends across technology subsectors. She also worked with 451 Research's Workforce Productivity & Collaboration team where she researched the future of work and emerging applications to enhance the employee experience. Melissa holds a bachelor’s degree in English Literature from Boston College and an MBA from ESADE, Ramon Llull University in Barcelona.
The shift reframes Salesforce from a UI-centric CRM specialist to a provider of an agentic architecture that supports humans and agents with new developer tooling, a cross-surface Experience Layer, and agent life-cycle controls to scale beyond single deployments.
One quarter into 2026, and Anthropic has already had a solid year — not only for its advancements in models, but also for its increasing dominance in the agentic experience space. How is its enterprise strategy evolving, and what differentiates Claude Code and Cowork?
So much is happening in generative AI that we plan to produce one of these roundups each month for as long as it makes sense to do so. We augment this with reports on product releases from vendors — as well as other developments — as part of our Market Insight service.
Our research shows that early enthusiasm for agentic AI is real, yet momentum is colliding with the technical and governance realities of running systems that reason and act with far less human oversight. Concerns around accuracy, data security and explainability consistently surface among the top barriers to adoption.
As enterprises advance along the agentic AI maturity curve, many are discovering how challenging it is to deliver reliably context-aware agents. Zep addresses these gaps by serving as the context layer for agents, unifying long-term memory, state and retrieval within a temporally aware graph.
The company is positioning itself as the AI engineering platform for agentic applications, combining open-source evaluations and tracing with an enterprise control plane and a purpose-built datastore to close the loop between development, evaluation and production reliability.
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