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These agents handle clearly defined tasks and workflows with high reliability.
They follow structured rules and triggers to complete work consistently
without human intervention.

These agents evaluate multiple options and choose the best next action
based on goals and constraints. They’re ideal when outcomes depend on
context, priorities, or trade-offs.

Learning agents improve over time by analyzing outcomes and feedback. As
they process more data, their accuracy and effectiveness increase without
constant retraining.

Autonomous agents plan, act, and execute with minimal supervision. Once
deployed, they manage tasks end-to-end and adapt to changing conditions
on their own.

These systems use multiple agents working together to solve complex
problems. Each agent handles a specific role while coordinating with others
to achieve shared outcomes.

Hierarchical agents operate in layers. High-level agents set direction and
strategy, while lower-level agents execute specific actions, enabling scale
without chaos.

Context-aware agents retain memory of past interactions, decisions, and
states. This allows them to respond intelligently over long workflows instead
of starting from scratch each time.

Tool-using agents interact with software, APIs, and databases to take real
action. They don’t just analyze. They trigger workflows, update systems, and
move work forward.