Why are small language models considered the future of agentic AI?

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.

Agentic AI differs from traditional automation by incorporating autonomous decision-making, goal-driven behavior, and adaptability. While traditional automation follows predefined rules and workflows, agentic AI systems use agents that can plan, reason, and collaborate dynamically to achieve complex objectives.

Business monitoring in agentic AI systems ensures that the AI agents are aligned with organizational goals and performance metrics. It involves tracking KPIs, detecting anomalies, and providing feedback loops to optimize decision-making and task execution. This helps maintain accountability and improves strategic outcomes.

Worker agents are specialized components within an agentic AI system that execute specific tasks or subtasks. They operate under the guidance of higher-level agents (like planner or manager agents). They are responsible for carrying out actions such as data retrieval, computation, or interaction with external tools and APIs. Their modularity allows for scalable and flexible task execution.

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.