{"id":93447,"date":"2026-05-07T08:00:00","date_gmt":"2026-05-07T15:00:00","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=93447"},"modified":"2026-05-06T14:54:03","modified_gmt":"2026-05-06T21:54:03","slug":"not-all-agentic-harnesses-are-equal","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/news\/stories\/not-all-agentic-harnesses-are-equal\/","title":{"rendered":"Not All Agentic Harnesses Are Created Equal"},"content":{"rendered":"\n

A new term has entered the AI lexicon: the agentic harness<\/a>. It\u2019s the scaffolding around a model that gives the AI access to the tools, data, and other elements that render it useful. <\/p>\n\n\n\n

Ethan Mollick, the Wharton business professor whose hands-on research on AI adoption has made him a leading voice in the field, describes the harness as what enables an AI agent<\/a> to \u201ctake actions and complete multi-step tasks on its own.\u201d<\/p>\n\n\n\n

If the AI foundational model is the engine, the harness is everything else: the chassis, the wheels, the drive shaft, the brakes. Increasingly, it\u2019s the harness and not the model that determines what actually gets done.<\/p>\n\n\n\n

That shift is already visible in a new class of products. Early examples of harnesses in practice include Anthropic’s Claude Code, which can generate, run, and refine code. Another is OpenClaw, an always-on agent that operates across applications with memory and persistence. Such products are defining a competitive environment that\u2019s moving beyond offering the most capable model to building the most effective harness around it.<\/p>\n\n\n\n

Not all harnesses are created equal, though, and the gap among them is larger than the current conversation acknowledges. What\u2019s more, those products, for all their promise, were built for use by individuals. <\/p>\n\n\n\n

Acting on behalf of an entire organization is a far more complex challenge. An enterprise agent needs to grasp more than what one employee wants. It must understand what the organization has collectively decided: the shared data and work history that give actions meaning and the policies and trade-offs that determine whether the agent has standing to act at all.

Deploy agents across an enterprise without that foundation and the problems quickly compound. Individual agents optimize for their own domain and none coordinate across the whole organization. Chaos ensues.\u00a0\u00a0\u00a0<\/p>\n\n\n\n

Where the real work lives<\/strong><\/h2>\n\n\n\n

Organizations don\u2019t act through a single mind. They operate with competing priorities, fragmented data, and decisions that require human authority, institutional memory, and hard-won consensus. <\/p>\n\n\n\n

Most of the harnesses we see today are built for environments where the work is self-contained and the finish line for tasks is clear. Coding is one obvious example. Others include booking travel, submitting and processing an expense report, and fielding customer inquiries across languages and systems. In these examples, the agent can handle a task from start to finish. The output is verifiable.<\/p>\n\n\n\n

But most work inside an enterprise is nowhere near that tidy. Consider what it takes to complete a complex procurement decision, handle a major contract negotiation, or run payroll. Each of these tasks touches multiple departments, relies on shared data and institutional history, and requires the negotiation of competing goals inevitable in any high-stakes decision. <\/p>\n\n\n\n

Without clearly defined priorities and encoded lines of authority, an agent can cross departments, trigger handoffs, and touch a dozen systems yet create more work than it completes. An agent without a robust-enough harness might optimize locally but create chaos collectively.\u00a0<\/p>\n\n\n\n

At Salesforce Futures<\/a>, we\u2019ve been asking what separates the harnesses that deliver from those that disappoint. The answer starts with agentic-loop reliability. Can an agentic system reliably complete a task from start to finish without a human overseeing each step? <\/p>\n\n\n\n

For a single user working on a discrete task, the loop is hard enough. Layer on the realities of an organization \u2014 conflicting agendas, distributed authority, data spread across dozens of systems \u2014 and the same loop becomes an order of magnitude harder to close reliably. <\/p>\n\n\n\n

The risk for more complex tasks is that a loop can break down at any of five stages:<\/p>\n\n\n\n