From Chatbots to Enterprise AI Agents
Enterprises are outgrowing basic chatbots and moving toward AI agent platforms that can execute real work, not just answer questions. Instead of a single conversational interface, organisations now run dozens or hundreds of enterprise AI agents, each tied to specific workflows such as ticket deflection, employee onboarding, customer service, or even code deployment. Analysts note that leaders are taking a crawl‑walk‑run path: starting with summarisation or simple ticket handling, then progressing to more agentic automation for complex processes. This shift is driving demand for platforms that can orchestrate, monitor, and govern agents across many systems. At the same time, the explosion of agents and underlying models has created operational “chaos” inside large environments filled with disparate SaaS tools. The emerging battleground is the AI agent platform or multi agent system that can bring this sprawl under control and provide a consistent operating layer for automation.
Google’s Gemini Enterprise Agent Platform: End-to-End Agent Deployment
Google’s Gemini Enterprise Agent Platform aims to be a turnkey AI agent platform for building, deploying, and managing agents at scale. The platform is designed to plug directly into existing enterprise systems so organisations do not have to rip and replace their current stack. Instead, Gemini Enterprise Agent enables AI agents to automate repetitive tasks, support complex workflows, and assist with decision‑making in areas like customer support, data analysis, content generation, and broader workflow automation. Google emphasises enterprise‑grade security, governance, and compliance, with controls over data usage, access permissions, and monitoring of agent behaviour to keep outputs aligned with policies and regulations. The focus is on reducing technical barriers to deploying enterprise AI agents across business operations, providing orchestration and deployment tools that let IT teams and developers scale agent usage while maintaining transparency and control.
Salesforce Agent Fabric: Control Plane for a Multi-Agent World
Salesforce’s Agent Fabric takes a different tack, starting from its CRM and productivity foothold and expanding into a cross‑stack control plane. Positioned as a command centre for enterprise AI agents, Agent Fabric discovers, governs, and orchestrates agents from many vendors, including Salesforce’s own Agentforce, Amazon Bedrock, Microsoft Foundry, OpenAI and homegrown models. New Agent Scanners automatically catalogue agents into a central registry, while a drag‑and‑drop Visual Authoring Canvas lets teams design multi‑agent workflows and human handoffs without heavy coding. Rules‑based guardrails via Agent Script for Agent Broker enable “guided determinism” for critical processes like escalations or refunds. An LLM Governance layer on AI Gateway centralises cost and compliance controls and introduces Trusted Agent Identity, requiring mobile approval for high‑stakes actions. In effect, Salesforce Agent Fabric turns the CRM-centric stack into a broader AI agent platform deeply embedded in daily customer and employee workflows.
Sakana AI’s Fugu: Multi-Agent System for Advanced Reasoning
Sakana AI’s Fugu targets a more technical slice of the market with a multi agent system optimised for coding, mathematics, and scientific reasoning. Rather than relying on a single large model, Fugu coordinates multiple frontier foundation models via multi-agent orchestration. It is built on years of collective intelligence research, including evolutionary model merging, The AI Scientist for autonomous research cycles, ShinkaEvolve’s evolutionary search over LLM‑generated programs, and AB‑MCTS, which showed multiple models collaborating via tree search can outperform single‑model approaches on difficult reasoning tasks. Initially exposed through an API, Fugu seeks to hide the complexity of juggling several model providers and API keys. Instead of requiring users to manually configure roles or workflows, the system dynamically routes tasks across a pool of specialised models. For engineering‑heavy teams, Fugu represents a new class of enterprise AI agents focused on deep reasoning rather than CRM workflows or generic business automation.
Workflow Pedigree, Open Platforms and Buyer Trade-Offs
As enterprises standardise on an AI agent platform, workflow pedigree and openness are becoming key differentiators. ServiceNow, for example, pitches itself as a “platform-of-platforms” that imposes order on sprawling application landscapes, with an AI Control Tower to observe every AI agent and workflow. Its AI‑native approach integrates data connectivity, workflow execution, security, and governance on a single platform, positioning ServiceNow as an operating system for enterprise workflows. Google’s Gemini Enterprise Agent focuses on secure deployment and integration, while Salesforce Agent Fabric emphasises cross‑vendor discovery, orchestration, and governance layered on top of CRM and existing SaaS. Fugu pushes toward flexible model orchestration for advanced tasks. Buyers need to evaluate how each stack integrates with current tools, supports governance and compliance, and mitigates vendor lock‑in. They must also consider who will build and maintain automations—deep developers, business admins, or a mix—since each platform targets different user profiles and operating models.