The way companies build systems is evolving. For years, we’ve focused on user-centered design—creating interfaces and processes that are intuitive for humans. But today, a new kind of participant has entered the conversation: AI agents.
These agents aren’t just assisting humans behind the scenes. They are beginning to act autonomously within workflows—analyzing information, making decisions, initiating actions, and communicating across systems. As this shift accelerates, forward-thinking companies are embracing a new mindset: designing for agents, not just users.
In the Salesforce ecosystem, agents are no longer theoretical. They exist today in the form of:
These agents consume data, interpret context, and take actions—all without a human manually clicking a button or filling out a form.
Traditional integrations have been designed with human operators in mind. But AI agents interact with data differently. They don’t need drop-down menus or visual cues—they need structured, reliable, and well-labeled data, real-time context, and robust APIs.
This has major implications for how we design and implement integrations.
Agents rely on APIs that are well-documented, consistent, and idempotent. They require clear status codes, meaningful error messages, and predictable outputs. Think beyond form submissions and build APIs with machine interpretation in mind.
Agents make decisions based on available context. Ensure that your data includes not just values, but metadata, timestamps, user roles, and relationships. Normalize formats and establish clear naming conventions so agents can understand what the data means.
Rather than waiting for human interaction, design your flows to allow agents to initiate processes—such as sending an invoice when a contract is signed, or escalating a case when a sentiment score drops below a threshold.
Agents function best in real-time. Design systems with event-driven architecture using platform events, Change Data Capture, or webhook triggers so that agents can respond as soon as something changes.
Here are a few real-world examples of agent-first design principles in action:
In each of these scenarios, the system isn’t waiting for human instruction. It’s observing, interpreting, and acting on its own.
When we design modern integrations at Zaghop, one of the most important questions we now ask is:
"How will an agent use this data or workflow?"
That question changes everything.
It changes how we name fields, how we handle exceptions, how we structure payloads, and how we build fallback logic. It forces us to think beyond the human interface and toward machine-enabled orchestration. This design philosophy is shaping how we approach implementations across industries—from healthcare to fintech to SaaS.
Designing for agents is no longer futuristic thinking—it’s now essential for building scalable, intelligent, and responsive systems. As AI continues to evolve, so must our design principles.
By thinking in terms of agents as first-class participants, companies can unlock new levels of automation, insight, and speed.
At Zaghop, this mindset is now core to every integration and implementation we deliver. Because tomorrow’s systems won’t just be used by people. They’ll be run by intelligent agents—ready to act, learn, and optimize in real time.