2025 Week 10 news

This week we look at:

• Salesforce’s new Agentforce 2dx developer platform launched at their TDX conference
• Emerging consensus around possible AGI timeframes and proposed national security frameworks
• OpenAI’s projected revenue growth with SoftBank partnership driving expansion

Agents get the Salesforce treatment

Salesforce held its developer conference, TDX, in San Francisco this week and the focus was the launch of its new agent developer experience Agentforce 2dx. The release builds on the core platform with enhanced developer tools, a marketplace for agents, and enterprise-grade management capabilities. Analysts suggest these updates could give Salesforce an edge over rivals in the agentic enterprise market, including Microsoft in particular.

In many hours of keynotes and demos, Salesforce laid out its vision for integrating AI with its established suite of business products. The highlights we spotted were as follows:

• Platform integration: Agentforce connects with Salesforce’s existing Sales and Service Clouds alongside Data Cloud. It interfaces with Slack for collaboration, Tableau for analytics, and MuleSoft for data integration. This breadth of integration is a competitive advantage and the case they make for keeping data in a single secure environment is compelling.
• Modern AI architecture: The platform is built around the ‘Atlas Reasoning Engine’ that seeks to leverage the recent evolution of LLMs into reasoners, and also leverage a range of small customer trained models from Salesforce including their 1B and 7B xLAM models specifically optimised for executing actions on the Salesforce platform.
• Headless agents: The new Agentforce API enables background, data-triggered AI processes that can function without constant human guidance. These agentic workflows with LLM-powered decision-making can interface with the more traditional automation flows already in the stack.
• Prototyping: A free Developer Edition includes access to Data Cloud, data storage, and a quota of LLM generations per hour.
• Pricing shift: Salesforce is moving from the $2/conversation agent cost to a credit-based consumption model for the API. This should make costs more manageable, but they are still going to be relatively high versus a custom build solution.
• Development tools: New tools include VS Code support, DX Inspector, YAML agent definitions, Apex integrations and the Testing Centre. This allows developers to simulate thousands of potential interactions, reducing manual testing requirements.
• Integration approach: Agentforce 2dx allows for incremental AI agent integration rather than wholesale system replacement. Salesforce reports 5,000 deals with Agentforce, though the depth of implementation and specific business outcomes remain to be seen.
• Partner ecosystem: The AgentExchange marketplace includes over 200 partners, including Google Cloud, DocuSign, and Box. This ecosystem approach resembles app stores and other developer marketplaces, with the usual challenges of maintaining quality and consistency.

What’s missing?

• Cross-agent collaboration framework: While individual agents can be built and deployed, the big gap is the lack of an architecture for how multiple agents should work together on complex tasks. Enterprise workflows often require handoffs between specialised systems, but the current release lacks sophisticated orchestration between agents. Multi-agent is slated for later in the year, however.
• Custom model and training capabilities: The ability to train agent models on proprietary enterprise data appears limited. Competitors and custom solutions can offer private fine-tuning and greater model variations. Whilst there is the concept of BYOM (bring your own model) it’s not clear how these can be used to power agents.
• Multi-modal capabilities: There was little information about how agents will handle image, voice, or video inputs. As enterprise use cases expand beyond text, these capabilities will become increasingly important.
• Edge deployment options: For latency-sensitive or offline scenarios, the ability to deploy lightweight agents at the edge isn’t addressed. This capability will be critical for manufacturing, field service, and similar applications.
• LLM cost optimisation: As token costs for large models remain significant, tools for automatically selecting the most cost-effective model for each task will be needed in the future.

Takeaways: We’ve written before about companies such as Klarna moving away from the traditional enterprise software stacks provided by Salesforce, Microsoft, Oracle and Workday etc. (Right on cue, Klarna’s CEO issued a clarification post on their phasing out of Salesforce this week). In reality, it’s more likely that a new layer of agentic intelligence is going to be built on those traditional digital business representations (customers, jobs, sales, tickets, invoices etc.), and Salesforce wants to retain their relevance with a broad ecosystem including developer tools, marketplace distribution, and enterprise governance. While key competitor Microsoft offers well-established productivity products, mature agent-building tools and robust cloud infrastructure, it lacks a cohesive digital business representation layer. This gives Salesforce an advantage in terms of modelling enterprise processes, customer journeys, and business relationships. Their representation, built over years of hoovering up customer data, provides the context that many agents will need to make meaningful business decisions. The real battle isn’t about which company builds the best chatbot, but which can create the most comprehensive representation or ‘digital twin’ of an enterprise in a form and with the tools and integrations to make the agentic workforce come alive. Salesforce’s combination of business process experience and new capabilities looks promising if costs can be controlled, and the roadmap quickly delivers more advanced features.