venturebeat
Why most enterprise AI coding pilots underperform (Hint: It's not the model)

Gen AI in software engineering has moved well beyond autocomplete. The emerging frontier is agentic coding: AI systems capable of planning changes, executing them across multiple steps and iterating based on feedback. Yet despite the excitement around “AI agents that code,” most enterprise deployments underperform. The limiting factor is no longer the model. It’s context: The structure, history and intent surrounding the code being changed. In other words, enterprises are now facing a systems design problem: They have not yet engineered the environment these agents operate in.The shift from assistance to agencyThe past year has seen a rapid evolution from assistive coding tools to agentic workflows. Research has begun to formalize what agentic behavior means in practice: The ability [...]

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venturebeat
Perplexity takes its ‘Computer’ AI agent into the enterprise, taking aim at Microsoft and Salesforce

Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]

Match Score: 94.20

venturebeat
Claude’s next enterprise battle is not models: it’s the agent control plane

New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]

Match Score: 91.53

venturebeat
OpenAI launches a Codex desktop app for macOS to run multiple AI coding agents in parallel

OpenAI on Monday released a new desktop application for its Codex artificial intelligence coding system, a tool the company says transforms software development from a collaborative exercise with a si [...]

Match Score: 80.75

venturebeat
GitHub leads the enterprise, Claude leads the pack—Cursor’s speed can’t close

In the race to deploy generative AI for coding, the fastest tools are not winning enterprise deals. A new VentureBeat analysis, combining a comprehensive survey of 86 engineering teams with our own ha [...]

Match Score: 78.59

venturebeat
Anthropic brings Mythos to the masses with Claude Fable 5, its most powerful generally available model ever

Anthropic today launched two new AI models — Claude Fable 5 and Claude Mythos 5 — marking the company’s first broad release of the powerful “Mythos-class” AI capabilities it previously kept [...]

Match Score: 76.04

venturebeat
From prototype to production: What vibe coding tools must fix for enterprise adoption

Presented by Salesforce Vibe coding — the fast-growing trend of using generative AI to spin up code from plain-language prompts — is quick, creative, and great for instant prototypes. But many arg [...]

Match Score: 68.21

venturebeat
The Agentic Reckoning: Enterprise AI organizations have a runtime problem, not a model problem — and most are building the wrong solution

In Q1 2026, VentureBeat's Pulse Research surfaced the “Governance Mirage”: the gap between the governance org charts enterprises had drawn and the control layers they had actually built. Fort [...]

Match Score: 63.46

venturebeat
OpenAI’s GPT-5.3-Codex drops as Anthropic upgrades Claude — AI coding wars heat up ahead of Super Bowl ads

OpenAI on Wednesday released GPT-5.3-Codex, which the company calls its most capable coding agent to date, in an announcement timed to land at the exact same moment Anthropic unveiled its own flagship [...]

Match Score: 61.94

venturebeat
Hiring specialists made sense before AI — now generalists win

Tony Stoyanov is CTO and co-founder of EliseAIIn the 2010s, tech companies chased staff-level specialists: Backend engineers, data scientists, system architects. That model worked when technology evol [...]

Match Score: 59.75