Agents

In Robility flow, agents act as intelligent decision-makers that use language models to choose and execute actions. Instead of following a fixed sequence, agents dynamically decide which tools, components, or data to use based on the user’s input and the context provided. They are designed for complex workflows where adaptive reasoning is needed, such as answering queries, retrieving knowledge, or invoking external APIs. By combining reasoning with available tools, agents bring flexibility, autonomy, and conversational intelligence to Robility flow pipelines. 

How Agents Work

1. User Input Interpretation – The agent processes natural language queries and interprets user intent using an LLM.

2. Decision-Making – Based on the context and available tools or components, the agent decides which action(s) to take.

3. Tool/Component Invocation – The agent can call tools (like search, APIs, calculators, or custom scripts) or trigger other Robility flow components to retrieve or process data.

4. Reasoning Loop – After receiving results, the agent evaluates whether the response fully addresses the query. If not, it iterates by selecting another tool or action until it builds a complete answer.

5. Response Delivery – Finally, the agent compiles and returns a cohesive, context-aware response to the user.

Why Agents Are Useful

1. Adaptive Behavior – Instead of rigid, step-by-step flows, agents dynamically adjust their actions to suit each query.

2. Tool Orchestration – Agents can coordinate multiple tools and components, selecting and combining outputs as needed.

3. Complex Problem Solving – Well-suited for tasks like multi-step reasoning, research, or knowledge retrieval where flexibility is essential.

4. Scalable Intelligence – Agents can grow with your project, handling anything from simple Q&A to sophisticated automation pipelines.

Agents vs. Components

1. Components are modular, predefined units (data processing, logic, LLM calls, etc.) that form the structure of a workflow.

2. Agents sit on top of components and tools, providing autonomy and reasoning so the system can adapt to user inputs without manually defining every step.

In essence, agents bring conversational intelligence and autonomy into Robility flow pipelines, enabling workflows that are not only structured but also self-directed, context-aware, and adaptive to real-world scenarios.