Agentic AI Agentic AI — autonomous AI agents
Agentic AI represents a breakthrough in how artificial intelligence is used — systems capable of reasoning, planning and executing complex tasks autonomously. Discover this paradigm and how to deploy it in your business.
Agentic AI in numbers
Autonomy, planning, action
What is agentic AI?
Agentic AI refers to artificial intelligence systems capable of acting autonomously to achieve complex goals. Unlike classic LLMs (ChatGPT, Claude used directly) that generate a response to a question, agentic AI agents plan their actions, use tools, retain context and adapt based on results to persist until the goal is reached.
Faced with the goal "analyse last month's customer reviews and identify recurring issues", an agentic agent will: connect to your review tool, extract data, analyse it with an LLM, group themes, generate a report and send it to the right owner — entirely autonomously.
The 4 key capabilities of an agentic AI agent
Planning: the agent breaks down a complex goal into sequential or parallel sub-tasks, determines optimal execution order and adapts the plan when obstacles arise.
Tool use: the agent calls APIs, queries databases, browses the web, sends emails, manipulates files — tools it uses dynamically according to the needs of the current task.
Contextual memory: the agent retains short-term context (within a session) and long-term context (persistent information across sessions) to maintain coherence on long tasks.
Reflection: the agent evaluates its own actions and results, detects errors and adjusts its strategy — it learns from attempts to improve its approach.
Agentic AI use cases in business
Research and synthesis of complex information (competitive intelligence, legal research), multi-source data analysis with automatic reporting, end-to-end process management (from intake to closure), assisted code development and debugging, and orchestration of multi-channel marketing campaigns.
Key points
What you need to know about agentic AI
More powerful than an LLM alone
An agentic agent accomplishes tasks impossible for an LLM alone — it plans, acts and persists on complex multi-step processes
Supervision required
Agentic agents need human oversight for critical actions — we always configure validation and alert mechanisms
Specialised frameworks
LangChain, LangGraph, AutoGen, CrewAI — mature frameworks for building robust, testable and maintainable agents
Critical testing phase
Agentic agents require extensive real-world testing before deployment — to avoid unwanted actions in production
What is agentic AI?
Agentic AI refers to AI systems capable of acting autonomously: they plan their actions, use tools, retain context and adapt based on results — far beyond a simple LLM that answers questions.
What is the difference between an LLM and an agentic AI agent?
An LLM answers a question. An agentic agent breaks down a complex goal, uses tools, acts on systems and persists across multiple cycles until the goal is reached. The agent acts, not just responds.
What are the risks of agentic AI in business?
Unwanted actions if limits are poorly defined, error accumulation on multi-step tasks. To mitigate: define the scope of action, implement human validation for critical actions and monitor logs continuously.
What tools are used to build agentic AI agents?
LangChain, LangGraph for complex agents, AutoGen for multi-agent systems, CrewAI to orchestrate teams of agents, and n8n for no-code agents. The choice depends on complexity and your technical constraints.