What is Agentic AI?
Agentic AI describes AI systems built around autonomous agents — software components that pursue goals, plan ahead, invoke tools, and iterate on results.
Unlike classic chatbots or rule-based automation (RPA), agents dynamically decide what to do next, based on context, available tools, and the evaluation of intermediate results.
Agentic AI is not a model, it’s an architecture: one or more LLMs plus tool definitions plus memory plus orchestration logic. That architecture is what turns “generate an answer” into “solve a task.”
Building blocks of an Agentic AI system
- LLM core — the language model as planner and decision-maker (Claude, GPT, Gemini, Llama, Mistral, …).
- Tool use — structured invocation of external functions: API calls, database queries, file I/O, code execution.
- Memory — short-term (context window) and long-term (RAG, vector stores, episodic memory).
- Orchestration — planning, reflection, and routing patterns. Frameworks: LangGraph, Microsoft Agent Framework (successor to AutoGen), CrewAI, Amazon Bedrock AgentCore.
- Multi-agent collaboration — specialised agents (researcher, coder, reviewer) work in parallel; a coordinator mediates.
- Model Context Protocol (MCP) — open standard for tool and data integrations. The MCP documentation2 compares it to a USB-C port for AI applications.
Agentic AI vs. classic AI applications
Quick differentiation:
- Chatbot — reacts to single requests; no goal pursuit across steps.
- RPA (Robotic Process Automation) — follows fixed rules and UI paths; in my view it typically breaks when things deviate.
- Workflow engine with LLM steps — fixed sequence; the LLM does detail work.
- Agentic AI — decides over flow and tools itself; iterates on results; stays goal-oriented even under deviation.
That flexibility brings new risks: a classic workflow is auditable by design, an agent is so only if the architecture is built for it. That’s precisely the interface with Agentic Engineering.
Agentic AI vs. generative AI
The two terms are often mixed up, but they describe different levels:
- Generative AI creates content on request — text, images, code. One call, one answer; what happens with the result is up to a human.
- Agentic AI uses generative models as its engine but pursues a goal over several steps: plan, call tools, check the result, adjust.
In short: generative AI answers, agentic AI acts. That is why the key question shifts from “Is the answer good?” to “What may the agent do, and who checks it?”.
Multi-agent systems: the next plateau
A single agent only scales to a certain complexity. Multi-agent systems split tasks into specialised roles:
- Researcher — searches knowledge sources and assembles findings.
- Architect — designs the solution structure.
- Builder — implements (code, configuration, document).
- Critic / Reviewer — checks against specification and quality criteria.
- Coordinator — orchestrates, mediates conflicts, keeps the roadmap in view.
Examples in practice: agent-based development tools like Kiro and Kiro CLI3, multi-agent coding pipelines with Claude Code. In my view, well-designed multi-agent systems can outperform single agents on complex tasks — but they have to be instrumented cleanly, otherwise you get endless loops, duplicated work, or unintended conflicts.
Model Context Protocol (MCP)
MCP was released by Anthropic as an open standard4 in November 2024 and, in my view, is becoming the lingua franca for Agentic AI integrations. Instead of wiring each tool through proprietary APIs, agents and tools speak a common protocol. For enterprise use that means:
- Auditability: every tool integration follows the same contract.
- Least-privilege access: fine permission boundaries per tool and call.
- Reduced development effort: build the MCP server once, multiple agents reuse it.
- Future-proofness: no lock-in to any specific agent framework.
Typical use cases for Agentic AI
- Software development: coding agents complete whole tasks (see Agentic Engineering).
- Customer support: agents classify, research, answer, and escalate — with full audit-trail transparency.
- Legal tech: case-file review, contract analysis, compliance checks. A related example, though generative AI rather than an autonomous agent: Legal Twin, developed by STP.One together with Storm Reply and Data Reply, supports case-file review according to a Reply case study5.
- Healthcare: possible use cases include supporting patient monitoring and diagnostics — in GxP-regulated environments only under human oversight (see Cloud Adoption in Healthcare).
- Operations / SRE: incident analysis, runbook execution, autonomous recovery.
Agentic AI shifts the question from “what can the model do” to “what can the system around the model do.” Architecture is the actual lever.
Frequently asked questions about Agentic AI
What is the difference between agentic AI and generative AI?
Generative AI creates content such as text, images or code on request — one call, one answer. Agentic AI uses generative models as its engine but pursues a goal over several steps: it plans, calls tools, checks the result and adjusts. Generative AI answers, agentic AI acts.
What are examples of agentic AI?
The best-known examples are coding agents such as Claude Code or Kiro: they read a task, change files themselves, run tests and submit the result for review. Others are multi-agent pipelines in which specialised agents research, build and review, and agents that reach company systems through the Model Context Protocol (MCP).
What’s the difference between a chatbot and Agentic AI?
A chatbot reacts to individual requests. Agentic AI pursues goals across multiple steps: it plans, invokes tools, evaluates intermediate results, corrects itself. The chatbot generates text; the agent solves tasks in the real world.
Which frameworks exist for Agentic AI?
At library level: LangGraph, Microsoft Agent Framework (successor to AutoGen, which is in maintenance mode), CrewAI, LlamaIndex Agents, OpenAI Agents SDK. On the cloud side: Amazon Bedrock AgentCore, Microsoft Foundry, Gemini Enterprise Agent Platform (formerly Vertex AI Agent Builder). For coding specifically: Claude Code, Cursor, Kiro. MCP is the integration protocol on top.
How secure are Agentic AI systems in enterprise use?
Security is a question of architecture, not of the model. Core principles: least-privilege per tool integration, auditable MCP interfaces, clear human approval gates on critical actions, sandboxing for code execution, full audit trails. Hallucination risk remains — mitigated by retrieval-augmented generation and strict specifications.
What does multi-agent collaboration mean concretely?
Multiple specialised agents work in parallel or sequence on subtasks — with clear roles (researcher, builder, reviewer, coordinator) and an orchestration layer that resolves conflicts and loops. On complex tasks this can outperform monolithic single agents, but it requires careful instrumentation.
How do Agentic AI and data protection / GDPR fit together?
Agentic AI potentially processes personal data via tools and memory. GDPR requirements (purpose limitation, data minimisation, retention) must be reflected in the architecture: central data classification, tool privilege separation, audit trails, privacy-by-design on memory stores. For sensitive workloads (health, finance, KRITIS) this complements the sovereignty discussion (see Digital Sovereignty).
When does Agentic AI pay off vs. classic automation?
When the workflow has variability that fixed rules can’t cover: unstructured input, context-dependent decisions, multi-stage research or synthesis tasks. For fully deterministic processes, classic automation remains cheaper, faster, and more auditable.
Sources
- Gartner: “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027”, press release, 25 June 2025. gartner.com
- Model Context Protocol: “What is the Model Context Protocol (MCP)?”, project documentation, accessed 27 September 2026. modelcontextprotocol.io
- Amazon Web Services: “Upgrade to Kiro”, Amazon Q Developer User Guide, accessed 27 September 2026. docs.aws.amazon.com
- Anthropic: “Introducing the Model Context Protocol”, blog post, 25 November 2024. anthropic.com
- Reply: “Leading German legal tech company revolutionizes file review with AI”, case study, n.d. reply.com
