Under the hood, laos whatsapp number data agentic workflows combine different technologies. When properly integrated, they create something far more powerful than any single component could achieve on its own.
AI agents form the core intelligence within agentic workflows. Built on LLMs, these agents provide reasoning, planning, and reflection capabilities. The semantic engine (typically the LLM) provides both reasoning capabilities and a conversational interface. This allows agents to seek clarification or approval when needed while still working autonomously on routine tasks.
While traditional AI requires constant guidance, agentic AI evolves and adapts to new situations without much human guidance and training.
Robotic process automation (RPA)
RPA enables AI agents global business opens up future opportunities to handle rule-based, repetitive tasks across different applications. This technology creates software robots that mimic human actions like data entry, transaction processing, and report generation.
In an agentic workflow, RPA serves as the hands that perform structured work. For example, after an AI agent extracts information from unstructured invoice documents, RPA automatically enters that data into accounting systems, eliminating tedious manual work.
NLP enables agentic workflows to understand and respond to human language. Here are a few critical functions that it takes care of:
- Interpreting user requests and questions.
- Understanding context and intent.
- Generating human-like responses.
- Processing text documents for information.
Machine learning algorithms
ML algorithms help agentic hong kong phone number workflows learn from experience and improve over time. These algorithms identify patterns in data, make predictions, and optimize processes without explicit programming.
This self-improvement capability means workflows get better with use. They learn which approaches work best in specific situations and adapt their strategies accordingly.
The performance of agentic workflows depends heavily on how agents are instructed. Prompt engineering shapes agent behavior through carefully designed instructions and examples.
Some advanced prompt engineering techniques include:
- Chain of thought: Guiding the agent through logical reasoning steps.
- Planning: Breaking complex tasks into manageable steps.
- Self-reflection: Enabling the agent to review and improve its work.