--- title: "Market Pulse" canonical: "https://current.tinyfish.ai/issue/4/market-pulse" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/4/market-pulse" issue: 4 section: "market-pulse" description: "Reading the agent ecosystem through a practitioner's lens" authors: ["Rina Takahashi"] published: 2025-11-20 updated: 2025-11-20 articles: 11 --- # TinyFish > CURRENT is a journal about AI agents in production, for the engineering and product leaders who have to make them work. Drawing on experience building enterprise web agents, it explains why the demo succeeded and the deployment didn't, and what that means for choosing vendors, briefing the board, and avoiding mistakes others have already made. [Read on the website]() ## Issue 4 — Market Pulse Reading the agent ecosystem through a practitioner's lens ### The Week's Architecture Signal - [When Governance Becomes a Control Point]() — By [Rina Takahashi]() · Published 2025-11-20 · Format: The Signal Microsoft announced Agent 365 on November 18, positioning itself not as an agent builder but as the infrastructure layer every enterprise agent must flow through. Third-party agents from Adobe, ServiceNow, and Workday now appear automatically in Microsoft's centralized registry. Each needs an Entra ID identity to operate. ### Reading the Forward Signal - [The Approval Threshold Just Shifted]() — By [Rina Takahashi]() · Published 2025-11-20 · Format: Field Notes Finance teams are approving automation projects they rejected 18 months ago. Same use cases, same workflows, same requirements. The pitch didn't change. The business case didn't change. But the CFO questions flipped from "can we justify this?" to "can we afford not to?" ### Surface Story, Deeper Pattern - [When Tracking Agents Isn't Enough]() — By [Rina Takahashi]() · Published 2025-11-20 · Format: Dual Lens Microsoft's Agent 365 launches this week with a registry of every agent running in your enterprise. Track what's deployed, monitor activity, catch shadow agents employees build without approval. When 230,000 organizations use Copilot Studio to build custom agents, centralized governance sounds necessary. But an agent can keep running, consuming resources, writing to dashboards while producing three-day-old data that looks current. Tracking agents isn't the same as understanding how they fail. - [The Coordination Problem Governance Can't Solve]() — By [Rina Takahashi]() · Published 2025-11-20 · Format: Dual Lens Three weeks before Microsoft launched its governance platform, 45 enterprise providers announced they're building something else entirely. The MACH Alliance isn't tracking agents centrally. They're creating protocols so agents from different vendors can coordinate across systems. Distributed collaboration instead of centralized control. But when agents from five vendors need to work together, and something breaks three steps into a workflow, the coordination layer can't tell you why. The web resists automation in ways protocols don't address. ### Production Gap Reality Check - [Microsoft Agent 365: Managing the Agent Sprawl]() — Published 2025-11-20 Microsoft announced Agent 365 last month, positioning it as the control plane for managing agents at enterprise scale. The pitch is elegant: extend your existing M365 infrastructure to agents. Use Entra for identity, Defender for security, Purview for compliance. No rebuilding required. ### Quiet Tech That Compounds Foundation model releases generate headlines. Capability demos generate buzz. Meanwhile, the infrastructure that determines whether your agent runs reliably at 3am on a Tuesday gets built quietly. Threading architectures that reduce CPU overhead. State management that survives session boundaries. Failover mechanisms that handle provider outages without application logic. The gap between demo and deployment lives here, in the boring optimizations that don't make good launch posts. These developments compound over time. They're foundational work that lets you stop thinking about infrastructure and start shipping features. Not exciting. Just necessary. - **Continuous Batching Delivers 23x Throughput** — [Source]() — Published 2025-11-20 Tag: Inference Optimization vLLM groups sequences at iteration level rather than waiting for batch completion. Result: 23x throughput improvement with lower latency. LLM serving is memory-bound, not compute-bound. System-level scheduling determines whether your economics work at scale. This optimization makes the difference between viable and impossible. - **Microsoft Framework Handles Enterprise State** — [Source]() — Published 2025-11-20 Tag: State Management Microsoft Agent Framework merges AutoGen's multi-agent patterns with Semantic Kernel's enterprise features. You get thread-based state management, type safety, filters, and telemetry. Long-running agents can persist context across sessions without building state management from scratch every time. Infrastructure that fades into the background. - **LangGraph State Machines Control Production Flows** — [Source]() — Published 2025-11-20 Tag: Workflow Orchestration Graph architecture with explicit state management, checkpoint APIs, and built-in error handling. Human-in-the-loop interventions work. Graceful degradation works. Node-level debugging works. When iterative planning loops run in production, silent failures aren't an option. LangGraph gives you the control plane you need. - **Global Routing Handles Provider Outages** — [Source]() — Published 2025-11-20 Tag: Infrastructure Resilience Azure routes traffic dynamically to datacenters with best availability. Salesforce Agentforce 3 fails over automatically between model providers during degradation. Your application doesn't need outage-handling logic. The infrastructure layer just keeps working. Reliability that doesn't require you to think about it. - **MLCEngine Cuts CPU Overhead to 3%** — [Source]() — Published 2025-11-20 Tag: Latency Engineering Standalone thread architecture separates engine loop from frontend processing. CPU overhead drops to 3% of batch decoding time. Users get 50-100+ tokens per second while maintaining request concurrency. Threading work nobody celebrates but everyone needs when agents have to feel responsive. - **Containerization and Rollback Finally Mature** — [Source]() — Published 2025-11-20 Tag: Deployment Tooling Model deployment tooling now includes version control for code, artifacts, and training datasets. Automated rollback to stable versions when issues emerge. Only 48% of AI projects reach production. Getting models deployed consistently is harder than training them. DevOps infrastructure addresses why. ## Other sections in Issue 4 - [Daily Brief]() - [Foundations]() - [Practitioner's Corner]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()