NextFin News - The landscape of enterprise technology is undergoing a seismic shift as the agent management software industry enters a period of intense competition and rapid innovation. On February 12, 2026, the sector reached a critical milestone with the emergence of Entire, a startup founded by former GitHub CEO Thomas Dohmke, which launched from stealth with $60 million in seed funding led by Felicis. This development coincides with major moves from incumbents like Salesforce, which has aggressively expanded its Agentforce platform to manage autonomous agents across sales, service, and marketing functions. According to The Futurum Group, these launches signal that incremental AI assistance is no longer sufficient; the industry is now moving toward a fundamental redesign of software development and enterprise workflows architected specifically for AI-native agents.
The competition is being driven by a structural mismatch in current technology stacks. Traditional developer tools and management platforms were designed for human-authored code and manual task execution. However, as AI agents begin to generate hundreds of thousands of lines of code and resolve over 74% of customer support cases autonomously—as seen in Salesforce’s internal deployments—the need for a dedicated orchestration and governance layer has become acute. Entire’s platform addresses this by introducing "Checkpoints," an open-source infrastructure that versions agent reasoning and session context directly in Git repositories. This allows for the tracking of not just what changed, but why an agent made a specific decision, providing the provenance and auditability required for enterprise-scale deployment.
The competitive field is currently split between two distinct philosophies: open infrastructure and integrated silos. Startups like Entire are positioning themselves as the "connective tissue" across various AI models, supporting Anthropic’s Claude Code and Google’s Gemini CLI. In contrast, incumbents like Salesforce and Atlassian are building end-to-end "agentic clouds" that leverage their existing data moats. According to Deloitte, the enterprise software market is expected to see 40% of applications integrated with task-specific AI agents by the end of 2026. This has sparked a "musical chairs" environment in mergers and acquisitions, with established players spending more on acquiring AI startups in 2025 than in the previous three years combined to bolster their orchestration capabilities.
This technological arms race is also redefining the human element of the workforce. As agents take over high-volume, low-value interactions, a new professional role is emerging: the Agent Manager. According to Harvard Business Review, companies like Salesforce and Walmart are already operationalizing this role to supervise fleets of digital employees. These managers focus on agent observability, refining prompts, and managing handoffs between machines and humans. The shift is moving Key Performance Indicators (KPIs) away from activity-based metrics toward outcome-driven orchestration. For instance, Salesforce’s sales development agents helped a small team increase meeting bookings from 150 per month to over 350 per week, generating $60 million in annualized pipeline.
Looking forward, the industry faces significant hurdles in scalability and security. Anthropic’s recent experiment, where 16 Claude Opus 4.6 agents autonomously built a C compiler, highlighted that while agents can handle complex systems, they still struggle with regressions and merge conflicts in codebases exceeding 100,000 lines. Furthermore, the rise of autonomous AI-powered cyberattacks—where agents can independently identify vulnerabilities and exfiltrate data—is forcing agent management software to prioritize "agentic security." Analysts predict that by 2027, the winners in this space will be those who can provide a unified context layer that ensures AI agents operate safely, transparently, and in tight alignment with strategic business intent.
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