Key Concepts
This page formalizes the core agentic AI concepts that underpin Ema's Builder Platform. These ideas inform everything from solution design to day-to-day operations.
Agents: The Building Blocks of Work
An agent is an autonomous process that combines planning, reasoning, and memory to complete a clearly scoped task. Ema provides three broad categories of agents:
| Category | Purpose |
|---|---|
| Data and Resources agents | Leverage knowledge graphs and other repositories to surface information relevant to the task. |
| Action agents | Decide on and execute operations within external applications, typically via APIs. |
| Skill agents | Apply domain-specific capabilities, drawing on user data, enterprise knowledge bases, external systems, or the open web to accomplish a specialist activity. |
AI Employees: Orchestrated Multi-Agent Workflows
An AI Employee is a higher-order construct that coordinates multiple specialized agents to perform an end-to-end role. The orchestration is handled by the Generative Workflow Engine (GWE), which offers a visual builder and a curated library of agents so that you can compose, observe, and iterate on complex workflows without writing code.
This framework allows Ema to deliver deeper automation, higher accuracy, and better explainability than standalone agents assisting humans in a role.
Why Multi-Agent Systems Are Better
Enterprise processes worth automating are typically repetitive and complex. AI Employees address both:
- Predictable and consistent outcomes. Agentic workflows repeatedly execute multi-step procedures with high confidence.
- Ability to handle complexity. Where single agents plateau at roughly four to five steps, AI Employees reason across many.
- Superior user experience. The architecture supports richer UI surfaces, explainability, and seamless human-in-the-loop collaboration.
Key distinction: Agents perform tasks; AI Employees perform work.
Multi-Agent Orchestration via GWE
The Generative Workflow Engine is the orchestration layer that coordinates multiple agents within a workflow. It manages data flow, dependencies, and execution order. AI Employees are built and orchestrated in GWE, drawing on a catalogue of reusable agents.
For a detailed treatment of GWE, see GWE Overview.
User Roles
Ema defines three roles that interact with AI Employees at different levels:
| Role | Key Responsibilities |
|---|---|
| Admin | Provision resources, security context, and configuration so an AI Employee can operate effectively. Admins are usually business users and subject matter experts. With Ema's framework, they are empowered to self-manage and improve their AI Employees. |
| Builder | Design and assemble the multi-agent workflow -- either from scratch or by customizing an AI Employee template. Not all deployments need a builder; if a pre-built AI Employee suffices, the admin can configure and launch it directly. |
| End User | Interact with the finished AI Employee to obtain outcomes. End users cannot alter the AI Employee's configuration. They may interact with Ema via chat, analytics dashboards, or their day-to-day SaaS applications. |
Human-in-the-Loop
Ema's agents can seek human help to disambiguate, assist, and approve their work mid-task. They may ask a question or present a partial or complete solution for approval.
When Does an Agent Request Human Input?
- Lack of information. Agents, like human employees, are not always equipped to complete a task. They may lack resources or need clarifications. Agents can be configured to proactively seek help as needed.
- Risk or compliance reasons. Critical tasks where errors are unacceptable or where a human sign-off is legally required. Agents can be configured to always seek human input for these cases.
Human-in-the-loop can be conversational (via chat) or structured (via dashboards). It unlocks the safe use of agentic AI across critical business flows.
Key Takeaways
- Agents are single-purpose building blocks; AI Employees orchestrate multiple agents into complete workflows.
- AI Employees are built in the Generative Workflow Engine (GWE), drawing on a catalogue of reusable agents.
- The architecture delivers predictable, explainable, and scalable automation for complex enterprise workflows.