The term AI agent is one of the most overused in 2026. On Agentic Platform it means something concrete: a software component that takes a task, has access to tools and data, and performs the task without human intervention until it is either done or escalates.
What an agent actually is
An agent is not a chatbot. A chatbot waits for questions and answers them. An agent gets a mandate (for example: respond to all inbound customer enquiries about booking status within 5 minutes), and uses that mandate actively. It pulls data from CRM, checks the booking system, formulates a response, and sends it. If it finds a case it does not have the mandate or information to solve, it escalates to a human.
Examples of agents we have built
A marketing agent that takes campaign briefs, pulls brand profile and product data, and generates draft posts. A sales agent that follows up cold leads with a personalized sequence based on CRM data. An operations agent that monitors defined KPIs and sends a daily summary with flags on deviations. A finance agent that matches supplier invoices against orders and extracts line-item data.
How agents coordinate
Agents share the same data layer and can call each other. Example: the sales agent signals a signed contract. The operations agent picks up the signal, opens a new customer account, and sends the welcome package. The finance agent issues the first invoice. All without manual triggering between steps.
The RAG layer
Each agent has access to the company's documents through a Retrieval Augmented Generation layer. That means agents answer from the company's own knowledge base: contracts, manuals, process documents. This is what lets a marketing agent write in your brand voice rather than a generic one.
When the agent cannot solve the task
Escalation is not an exception. It is a built-in part of the design. Each agent has explicit rules for when to escalate: new customer type, missing data, cost above a threshold, regulatory judgment required. The escalation goes to the right human with full context, not to a general inbox.
What does not work yet
Agents are not magic. They work best on defined workflows with clear data sources. Complex strategic decisions, interpersonal negotiation, and tasks requiring judgment without clear rules are still human work. We design the agent for what it can actually do well, and let humans keep what it cannot.
How to get started
We recommend starting with one agent for one well-defined workflow. The Discovery phase maps which one delivers the most value fastest. Once that agent is performing, we scale up to the next.