How do worker agents contribute to the processes of an AI system?

Worker agents are responsible for executing specific tasks. They are typically directed by planner or orchestrator agents and focus on performing well-defined actions such as data processing, API calls, or user interactions.

Small language models are gaining traction in agentic AI due to their efficiency, lower computational cost, and ease of deployment. They can be fine-tuned for specific tasks, making them ideal for use as worker agents or task-specific modules. Their lightweight nature also enables real-time responsiveness and integration into edge devices or constrained environments.

The agent converts raw inputs into a structured, up-to-date view of the task and environment so it can plan next steps. Concretely, it:

• Ingests signals: user message, prior tool outputs, files, UI/sensor state.

• Parses & normalizes: cleans text, validates JSON/tables, transcribes/OCRs if needed.

• Infers intent & entities: extracts goals, constraints, slots (who/what/when), success criteria.

• Grounds to real objects: links mentions to canonical IDs (docs, contacts, issues, items).

• Retrieves context: pulls only the most relevant knowledge/memory snippets (RAG).

• Assesses affordances: identifies feasible tools/actions given the current state.

• Flags uncertainty/policy risks: notes ambiguities, missing info, permissions needed.

• Updates world state: emits a concise, machine-usable snapshot for the planner.

Output handed to planning: a structured task brief, the top-K supporting context, current environment state, feasible next actions, and open questions.

An orchestrator agent coordinates other agents/services to achieve a goal. Think of it as the project manager in a multi-agent or multi-service system.

The role of memory in an agentic AI system is retaining context, learning from past experiences, and making better & informed decisions over time.