February 10, 2026Product UpdateSource: Salesforce Blog

Salesforce Launches Agentforce for Autonomous CRM Workflows

Salesforce has launched Agentforce, a platform that enables enterprises to build and deploy autonomous AI agents for sales, customer service, marketing, and commerce workflows, deeply integrated with the Salesforce data platform.

Salesforce has launched Agentforce, a comprehensive platform for building autonomous AI agents that operate within the Salesforce ecosystem. The platform enables businesses to create agents that handle end-to-end workflows in sales, customer service, marketing, and commerce without human intervention.

Agentforce agents have native access to Salesforce's data platform, meaning they can query customer records, update opportunities, send emails, create cases, and trigger workflows using the full context of the organization's CRM data. This deep integration gives agents knowledge that external AI tools lack.

The platform includes pre-built agent templates for common use cases including lead qualification, customer support triage, contract renewal, and campaign optimization. Each template can be customized through natural language instructions, allowing business users to modify agent behavior without coding.

Salesforce has implemented a Trust Layer that governs all agent actions. The Trust Layer includes data masking to prevent agents from exposing sensitive information, action limits that prevent agents from making changes above configurable thresholds, and audit trails that log every decision and action for compliance.

Agentforce is available as an add-on to Salesforce Enterprise and Unlimited editions at $2 per conversation. Salesforce CEO Marc Benioff described Agentforce as representing a shift from CRM as a system of record to CRM as a system of action, predicting that AI agents will handle the majority of routine CRM interactions within three years.

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March 13, 2026Industry

AI Agents Market Reaches $15 Billion as Enterprise Adoption Surges

The global market for AI agents — autonomous AI systems that can plan, execute, and iterate on complex multi-step tasks — has reached $15 billion in annual spending, according to a new report from McKinsey. This represents a 200% increase from 2025, driven by enterprise adoption of agentic AI for customer service, software development, data analysis, and business process automation. The report identifies three tiers of AI agent adoption: basic agents that handle single-step tasks like email responses and appointment scheduling (adopted by 65% of enterprises), intermediate agents that manage multi-step workflows like report generation and data pipeline management (35% adoption), and advanced agents that autonomously execute complex processes like code deployment and financial analysis (8% adoption). The largest spending categories are customer service agents ($4.2B), coding agents ($3.8B), and data analysis agents ($2.5B). McKinsey projects the market will reach $45 billion by 2028 as agent reliability improves and enterprises become more comfortable delegating complex decisions to AI. Key enabling platforms include OpenAI's Agents SDK, Anthropic's Claude computer-use capabilities, and LangChain's agent framework. The report warns that agent governance and monitoring remain underdeveloped, with most enterprises lacking adequate oversight mechanisms for autonomous AI actions.

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Microsoft 365 Copilot Gets Custom AI Agents and Actions

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March 12, 2026Analysis

GPT-5.2's Agentic Mode Transforms Enterprise Workflows

OpenAI's GPT-5.2 introduced a fundamentally new approach to agentic task completion that is already transforming enterprise workflows. The model can now maintain coherent plans across 50+ sequential tool calls with parallel execution, reducing latency in complex automation pipelines by up to 60%. Early enterprise adopters report that GPT-5.2's agentic mode handles tasks like multi-step data analysis, cross-platform content publishing, and automated code review workflows that previously required custom orchestration code. The key innovation is what OpenAI calls deliberative alignment — a training approach that lets the model dynamically allocate compute to harder sub-tasks while breezing through simpler ones. This means a single agentic session can handle both quick lookups and deep reasoning without manual configuration. Several Fortune 500 companies have reported 40-70% time savings on analyst workflows by deploying GPT-5.2 agents through the API. However, reliability remains a concern — OpenAI acknowledges a 3-5% failure rate on chains exceeding 30 steps, and enterprise deployments require human-in-the-loop checkpoints for critical decisions.