Salesforce launches Agentforce Operations to fix the workflows breaking enterprise AI
Our take
Salesforce's recent launch of Agentforce Operations shines a light on a critical challenge facing enterprise AI teams: the workflows that underpin these advanced systems are often poorly designed for the agents meant to execute them. As organizations push deeper into the realm of automation, they encounter a frustrating reality: tasks frequently fail, and handoffs become problematic. This disconnect is not merely a technical hiccup; it signifies a fundamental misalignment between human-centric design and the capabilities of AI agents. The emergence of workflow execution control planes, as seen in Salesforce's approach, represents a significant evolution in how enterprises can optimize and structure their processes for AI integration. This shift is crucial as it sets the stage for a more deterministic and reliable interaction between AI agents and back-office systems, which is essential for successful implementation.
The importance of this development cannot be overstated. Many enterprise workflows have evolved over time, often characterized by vague steps and implicit knowledge that rely heavily on human judgment. This historical context creates a daunting challenge for AI systems, which function best in environments with clear, structured processes. Salesforce's focus on breaking down workflows into explicit tasks for agents is not just about improving efficiency; it is about fundamentally rethinking how organizations operate. As highlighted by Salesforce's senior vice president of Product, Sanjna Parulekar, the clarity provided by these new control planes ensures that agents are not left to navigate poorly defined pathways, which can lead to increased costs rather than the promised efficiencies. This insight is particularly relevant when considering the potential for organizations to leverage these new tools to create more robust and transparent processes.
However, introducing a deterministic structure to workflows is not a panacea. The risk remains that merely codifying existing workflows can lock in inefficiencies at scale. As Brandon Metcalf, CEO of Asymbl, pointed out, the focus must shift from merely executing tasks to ensuring that those tasks are coherent and aligned with organizational goals. The governance of these workflows becomes paramount. Organizations must take a hard look at their current processes, determining not only what works but also what needs to be reimagined. This introspection is essential, as the responsibility for task completion and success must be clearly defined among team members or AI agents. The challenge is not just about replacing human judgment with machine execution; it is about creating an environment where both can thrive together.
This moment in the evolution of enterprise AI prompts important questions for the future. As organizations increasingly adopt platforms like Agentforce Operations, how will they ensure that their workflows evolve in tandem with technological advancements? Will they have the foresight to adapt their processes, or will they risk perpetuating outdated practices under a new guise? The journey toward integrating AI into workflows is not merely a technical challenge but a cultural one, requiring organizations to foster a shared understanding of goals and outcomes among all participants, human and agent alike. As we look to the future, the success of enterprises in this rapidly changing landscape may depend on their ability to embrace this duality of human and machine collaboration.
In conclusion, Salesforce's initiative highlights a critical inflection point in the relationship between AI and enterprise workflows. As organizations navigate this complex landscape, the balance between automation and human oversight will be key to unlocking the true potential of AI. The path forward will demand not only innovative solutions but also a commitment to rethinking how work is structured and who is responsible for its success. For readers invested in the future of enterprise automation, this journey is one to watch closely.
Enterprise AI teams are hitting a wall — not because their models can't reason, but because the workflows underneath them were never built for agents. Tasks fail, handoffs break, and the problem compounds as organizations push agents deeper into back-office systems. A new architectural layer is emerging to address it: workflow execution control planes that impose deterministic structure on processes agents are expected to run.
One of the companies bringing this to the forefront is Salesforce, with a new workflow platform that turns back-office workflows into a set of tasks for specialized agents to complete. Users can upload their processes or use one of the set Blueprints provided by Salesforce, and Agentforce Operations will break it down for agents.
Salesforce senior vice president of Product, Sanjna Parulekar, told VentureBeat in an interview that the problem is that many enterprise workflows are not built for agents. “What we’ve observed with customers is that a lot of times, the brokenness in a process is probably in your product requirements document,” Parulekar said. “So when that’s uploaded into a product, it doesn’t quite work. We can optimize it and cut out some things and replace it with an agent.”
Without this control panel layer, enterprises could risk deploying agents that increase cost rather than fix their workflow problems.
Making the workflow work for agents, not just humans
Enterprises deploying agents are learning a costly lesson: Their workflows were designed around human judgment gaps, not machine execution. Processes that evolved through years of workarounds — loosely defined steps, implicit decisions, coordination that depends on individuals knowing what to do next — break when agents are asked to follow them literally.
Even with all of an enterprise’s context at its fingertips, AI systems will have difficulty completing tasks if it is not clear what it’s supposed to do.
Parulekar said her team found that focusing on what makes the process tick and breaking it down into more explicit steps and workflows makes the system more deterministic. Then, when platforms like Agentforce Operations introduce agents, those agents already know their specific tasks.
“It forces companies to rethink their processes and introduces observability into the mix because of the session tracing model in the system,” she said.
Parulekar said human checks can be built into the system, so the process is more transparent.
What makes this approach different from other workflow automation offerings is that it doesn’t rely on agents to decide what to do next; the system does. Unlike more traditional automation tools that route tasks and agents on probabilistic decision-making, this enforces execution on a more pre-defined, deterministic structure.
The problem it introduces
Codifying a workflow doesn't fix a broken one. If a process has flawed steps, encoding it for agents locks in the problem at scale. And once workflows are distributed across agents, the challenge shifts from execution to governance: who owns the process, who validates it, and how it evolves when business conditions change.
It puts the onus on teams to take a hard look at what works for them and what doesn’t.
Organizations need to consider that, along with the execution control plane offered by platforms like Agentforce Operations, someone should be made responsible for task completion and success.
Brandon Metcalf, founder and CEO of workforce orchestration company Asymbl, told VentureBeat in a separate interview that the key to both humans and agents following a workflow is a shared goal.
“You have to understand the goal or the agent or human won’t complete the task successfully,” Metcalf said. “Someone has to manage that outcome that has to be delivered. It can be a person or an agent.”
The bottleneck has moved. As Metcalf framed it, the question is no longer whether agents can reason through a task, it's whether the workflow underneath them is coherent enough to execute. For enterprises that built their processes around human judgment and institutional memory, that's a harder fix than swapping in a smarter model.
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