Market Pulse
Reading the agent ecosystem through a practitioner's lens
Market Pulse
Reading the agent ecosystem through a practitioner's lens
The Infrastructure Layer Land Grab Behind Workday's Acquisition Spree
Workday has acquired six AI-related companies since February. Six infrastructure platforms in under a year—HiredScore, Evisort, Sana Labs for $1.1 billion, Flowise, and now Pipedream with its 3,000 pre-built connectors. That's not normal acquisition velocity for enterprise software.
Look at what Workday is actually buying: not reasoning capabilities or elegant interfaces, but authentication layers, integration platforms, connection infrastructure. The unglamorous plumbing that sits between agents and the fragmented reality of enterprise systems. The companies moving fastest aren't building better AI. They're assembling control points for where agents can operate at all.
The Infrastructure Layer Land Grab Behind Workday's Acquisition Spree
Workday has acquired six AI-related companies since February. Six infrastructure platforms in under a year—HiredScore, Evisort, Sana Labs for $1.1 billion, Flowise, and now Pipedream with its 3,000 pre-built connectors. That's not normal acquisition velocity for enterprise software.
Look at what Workday is actually buying: not reasoning capabilities or elegant interfaces, but authentication layers, integration platforms, connection infrastructure. The unglamorous plumbing that sits between agents and the fragmented reality of enterprise systems. The companies moving fastest aren't building better AI. They're assembling control points for where agents can operate at all.
Where AI Agent Value Is Actually Concentrating
When Microsoft merged two orchestration frameworks last month, most coverage focused on technical architecture. From our operational position running millions of web agent sessions, we saw a different signal: the problems that seemed defensibly complex six months ago are converging into common patterns.
Managing authentication across thousands of sites simultaneously shows you something about which problems stay hard and which ones don't. The capital flows in November—over $3.5 billion into AI infrastructure—suggest smart money is betting on layers most vendors aren't emphasizing. Where is defensibility actually building in this market?
Where AI Agent Value Is Actually Concentrating
When Microsoft merged two orchestration frameworks last month, most coverage focused on technical architecture. From our operational position running millions of web agent sessions, we saw a different signal: the problems that seemed defensibly complex six months ago are converging into common patterns.
Managing authentication across thousands of sites simultaneously shows you something about which problems stay hard and which ones don't. The capital flows in November—over $3.5 billion into AI infrastructure—suggest smart money is betting on layers most vendors aren't emphasizing. Where is defensibility actually building in this market?
Rina Takahashi
Rina Takahashi, 37, former marketplace operations engineer turned enterprise AI writer. Built and maintained web-facing automations at scale for travel and e-commerce platforms. Now writes about reliable web agents, observability, and production-grade AI infrastructure at TinyFish.
Surface Story, Deeper Pattern
Why 16,000 MCP Servers Appeared in Less Than a Year
Sixteen thousand servers in less than a year. That growth didn't come from marketing—it came from architectural decisions that removed every barrier between developers and deployment. Anthropic's Model Context Protocol optimized for velocity over control, autonomy over gatekeeping. The design worked. Integration friction disappeared. Developers built what they needed without waiting for vendor roadmaps. Understanding why this happened reveals what changes when protocols prioritize developer experience above everything else.
The Organizational Work MCP's Architecture Doesn't Include
The architectural choices that enabled MCP's rapid growth assumed something most organizations don't have: the security infrastructure to handle distributed responsibility. Forty-three percent of publicly available servers contain command injection vulnerabilities. The protocol's design left security vetting, credential management, and operational governance for deploying teams to solve. That organizational work—building capabilities most teams lack—turns out to be harder than writing the servers themselves. The velocity came with a bill.
Why 16,000 MCP Servers Appeared in Less Than a Year
Sixteen thousand servers in less than a year. That growth didn't come from marketing—it came from architectural decisions that removed every barrier between developers and deployment. Anthropic's Model Context Protocol optimized for velocity over control, autonomy over gatekeeping. The design worked. Integration friction disappeared. Developers built what they needed without waiting for vendor roadmaps. Understanding why this happened reveals what changes when protocols prioritize developer experience above everything else.
The Organizational Work MCP's Architecture Doesn't Include
The architectural choices that enabled MCP's rapid growth assumed something most organizations don't have: the security infrastructure to handle distributed responsibility. Forty-three percent of publicly available servers contain command injection vulnerabilities. The protocol's design left security vetting, credential management, and operational governance for deploying teams to solve. That organizational work—building capabilities most teams lack—turns out to be harder than writing the servers themselves. The velocity came with a bill.
Why 16,000 MCP Servers Appeared in Less Than a Year
Sixteen thousand servers in less than a year. That growth didn't come from marketing—it came from architectural decisions that removed every barrier between developers and deployment. Anthropic's Model Context Protocol optimized for velocity over control, autonomy over gatekeeping. The design worked. Integration friction disappeared. Developers built what they needed without waiting for vendor roadmaps. Understanding why this happened reveals what changes when protocols prioritize developer experience above everything else.
The Organizational Work MCP's Architecture Doesn't Include
The architectural choices that enabled MCP's rapid growth assumed something most organizations don't have: the security infrastructure to handle distributed responsibility. Forty-three percent of publicly available servers contain command injection vulnerabilities. The protocol's design left security vetting, credential management, and operational governance for deploying teams to solve. That organizational work—building capabilities most teams lack—turns out to be harder than writing the servers themselves. The velocity came with a bill.
Production Gap Reality Check
Microsoft announced Work IQ at Ignite as the intelligence layer that makes Copilot "know you, your job, and your company inside and out." APIs are live now. Some features already power Copilot enhancements. Others arrive in preview early 2026.
Here's what the launch materials gloss over: this only works if proper permissions, data quality, and governance already exist in your Microsoft 365 environment. Work IQ's value fundamentally requires the data hygiene you've been postponing for years.
At scale, you're not deploying AI. You're auditing every SharePoint permission, cleaning metadata, establishing governance protocols that should have existed before cloud migration. Microsoft is selling the engine while assuming you've already built the road. Most enterprises are discovering they haven't.
Microsoft announced Work IQ at Ignite as the intelligence layer that makes Copilot "know you, your job, and your company inside and out." APIs are live now. Some features already power Copilot enhancements. Others arrive in preview early 2026.
Here's what the launch materials gloss over: this only works if proper permissions, data quality, and governance already exist in your Microsoft 365 environment. Work IQ's value fundamentally requires the data hygiene you've been postponing for years.
At scale, you're not deploying AI. You're auditing every SharePoint permission, cleaning metadata, establishing governance protocols that should have existed before cloud migration. Microsoft is selling the engine while assuming you've already built the road. Most enterprises are discovering they haven't.
AI agents understand organizational context through unified data, memory, and inference across your entire Microsoft 365 environment automatically.
APIs work but respect existing permissions and labels, meaning your messy governance produces equally messy AI results today.
Launch materials showcase intelligent agents while quietly requiring data architecture audits most enterprises haven't started, let alone completed successfully.
Teams need Foundry SDK expertise, observability design, staged rollouts with kill switches, plus months of permission cleanup before value.
Mature architecture, not vaporware. But Microsoft sells infrastructure assuming you've done the hard governance work. You probably haven't.
Quiet Tech That Compounds
The infrastructure that matters most rarely makes headlines. Parameter counts and benchmark leaderboards get the attention. Production systems depend on something else entirely: observability that catches issues before users do, memory management that prevents OOM crashes at 3 AM, error handling that fails gracefully instead of spectacularly.
This is the quiet work that compounds. Standards that let agents actually interoperate. Distributed serving that scales without heroics. Security monitoring built for threats that didn't exist two years ago. None of it demos well. All of it determines whether your agent runs reliably or becomes someone's weekend firefighting story.
Here's what's maturing in the background.
The infrastructure that matters most rarely makes headlines. Parameter counts and benchmark leaderboards get the attention. Production systems depend on something else entirely: observability that catches issues before users do, memory management that prevents OOM crashes at 3 AM, error handling that fails gracefully instead of spectacularly.
This is the quiet work that compounds. Standards that let agents actually interoperate. Distributed serving that scales without heroics. Security monitoring built for threats that didn't exist two years ago. None of it demos well. All of it determines whether your agent runs reliably or becomes someone's weekend firefighting story.
Here's what's maturing in the background.