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Kronos
Ekko AI Assistant
Glossary
An AI agent is a software system that uses an LLM as its reasoning engine to perceive its environment, make decisions, take actions, and work toward a goal — autonomously.
The Definition
A chatbot responds to your message. An AI agent pursues a goal. The difference is autonomy — an agent can take a task, break it into steps, use tools to gather information, make decisions along the way, and deliver a result without you directing each action.
The LLM is the reasoning engine. It reads context, understands what needs to happen, and decides what to do next. But the agent is more than the LLM — it's the LLM plus tools, memory, and the ability to act on external systems.
That combination is what makes AI agents genuinely useful for business operations. They don't just answer questions. They do work. When multiple agents coordinate to handle complex workflows, this becomes agentic AI — the next evolution of AI automation.
Core Components
The agent receives inputs — text, documents, API responses, database records, images. It perceives its environment through whatever data sources it has access to.
The LLM at the core reads the inputs, understands context, evaluates options, and decides what to do next. This is what separates agents from scripts — genuine inference, not pattern matching.
The agent calls external systems — APIs, databases, search engines, file systems, communication platforms. Tools are how the agent acts on the world, not just thinks about it.
The agent maintains context across steps — what it found, what decisions it made, what actions it took. Memory lets each step build on the last, enabling coherent multi-step work.
The agent produces outputs — a written verdict, a routed ticket, an updated record, a sent message, a triggered workflow. The action is the point. Everything else is in service of it.
Types of AI Agents
One agent, one task. Focused, fast, and easy to reason about. Best for well-defined processes with a clear start and end — like processing a single document or triaging a single alert.
An orchestrated team of specialized agents. One agent plans, others execute specific subtasks, a final agent synthesizes results. Handles complex workflows that benefit from parallelism and specialization.
The agent does the work, a human approves before action is taken. Ideal for high-stakes decisions where you want AI speed and consistency but human accountability at the final step.
Examples
These are the kinds of agents Ekko builds and deploys.
Reads incoming support requests, classifies intent, retrieves relevant account data, drafts a response, and either sends it or routes to a human based on complexity.
Reads a security alert, queries three systems for context, reasons about severity, writes a structured verdict with evidence, and routes to the right analyst or closes as false positive.
Reads PDFs in any format, extracts structured data, validates against business rules, flags exceptions with specific notes, and routes for approval or auto-processes within policy.
Continuously monitors systems and communications for policy violations, classifies findings by severity, generates audit-ready reports, and escalates issues that require human review.
How Ekko Builds AI Agents
Every AI agent Ekko builds runs on EVAA — our modular agentic AI framework. EVAA handles orchestration, tool integration, memory management, and observability. Security is built in: least privilege, encrypted data handling, audit logging, and prompt injection mitigation. You get a production system, not a prototype.
See how we buildTalk to an expert. We'll identify the right processes for AI agents and show you exactly what's possible.
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