Agentic software: what it is and why your next platform will be operated by agents

Agentic software is software built to be operated by both people and AI agents, with the same powers for both. In practice, this means that everything you do by clicking in the interface, an agent can do by receiving an instruction in natural language: create a dashboard, build a segment, adjust a workflow, query the database. In this article, we explain how this type of system works, what advantages it brings to a marketing and sales operation, and what new question it puts into the evaluation of any platform.

Agentic AI and agentic software: the difference that matters

The two terms look like siblings, but they look at opposite sides of the same scene. Agentic AI is the intelligence that acts with autonomy: it receives a goal, plans steps, uses tools, and verifies the result, as Salesforce defines it. The focus is on the agent. Agentic software looks at the other side: it is the platform prepared to receive that agent. An AI capable of acting is of no use if the system where it needs to work only accepts human clicks.

The distinction becomes visible in the architecture. In most platforms, AI is an attached feature: a chat in the corner of the screen that answers questions, but does not operate the product. In an agentic system, the agent has access to the same actions as the user, through the same data model, under the same rules.

How agentic software works in practice

At Laiki, human and agent operate the same platform through two doors. The person uses the interface: creates dashboards, builds segments, designs workflows, queries leads. The agent uses MCP (Model Context Protocol, the open protocol that connects AI assistants to external systems) and performs the same operations: creates the same dashboards, builds the same segments, queries the same leads. The two doors lead to the same room.

This changes the type of request you can make. Instead of “explain to me how to create a dashboard,” the request becomes “create this quarter's acquisition dashboard.” Instead of exporting a list to analyze elsewhere, the request becomes “which leads in the high-engagement segment have not yet been contacted?” The agent executes inside the platform, and the result stays there, visible to the whole team. The same goes for routines: the agent designs the workflow that sends the conversion to the media platform, builds the segment that feeds the contact cadence, and adjusts the widget that ended up with the wrong metric. To understand the connection mechanics, see the Laiki MCP installation tutorial.

The advantages of this type of solution

Speed. The agent builds in minutes what would take an afternoon of clicks: a dashboard with ten widgets, a segment with compound rules, a behavior report. The team's time shifts from execution to decision.

Continuity. What the agent creates does not evaporate when the conversation ends. The dashboard stays on the platform, editable through the interface, visible to those who have never talked to an AI. It is the difference between a chat answer and an asset of the operation.

No dependence on a single AI. Since the entry point is an open protocol, any compatible agent operates the system: if one assistant goes down, another takes over, and the operation does not stop. We have already shown why this matters in the article about the risk of relying on a single AI.

Human control. Everything the agent does is recorded and editable. The person reviews the dashboard, adjusts the segment rule, undoes what did not make sense. The agent speeds things up; governance stays with the team.

What an agent needs to operate well

Here comes the honest limit. An agent without context guesses, and guessing with data is dangerous. To operate well, it needs two things: defined permissions (what it can read, what it can create) and real context from the operation, such as the event map, the funnel stages, and the company's vocabulary. Without that, no agentic software delivers what it promises, because the agent's intelligence does not replace the organization of the database. The integrations that feed this database remain the foundation of everything.

The new question in evaluating platforms

For years, the evaluation question was “does this tool have AI?” It has aged: today every tool has some attached AI feature. The question that separates platforms now is a different one: can an agent operate this software? Can it create, and not just answer? Is what it creates auditable? Does it work with more than one assistant? Those who answer yes to these questions are ready for a team that works side by side with agents. Those who answer no are left with a friendly chat in the corner of the screen.

Frequently asked questions

Are agentic software and agentic AI the same thing?

No. Agentic AI is the intelligence that acts with autonomy toward a goal. Agentic software is the platform designed so that these agents can operate it, with the same actions available to humans and to AIs.

Do I need to know how to code to use agentic software?

No. Operation by agent happens in natural language: you describe what you want and the agent executes it on the platform. The traditional interface remains available for those who prefer the manual path.

Can the agent do something on the platform without my knowing?

In a well-designed agentic system, no. The agent operates within the defined permissions, and everything it creates is recorded, visible, and editable. Human review is part of the model, not a patch.

Want to see an agent operating real data? Discover Laiki's AI analysis and connect the assistant your team already uses to your operation.