First, what is not an agent
A basic chatbot takes a question and returns an answer. It does not take actions or pursue goals. Useful, but limited. Both AI agents and agentic AI go further: they do things.
What is an AI agent?
An AI agent is a system built around a language model that can use tools to complete a specific task. It receives a goal, decides which tool to call (search a knowledge base, look up an order, send an email), observes the result and continues until the task is done or it needs help.
Examples:
- A support agent that answers questions and checks order status on WhatsApp.
- A document agent that reads invoices and enters them into the ERP.
- A sales agent that qualifies leads and books meetings.
What is agentic AI?
Agentic AI describes systems that act with greater autonomy across multi-step workflows. They typically combine several agents, each with a role, coordinated by a planner or orchestrator. They keep state over long-running processes, check their own work and involve humans at key decision points.
Example: an RFQ-to-quote system for a fabrication company.
- An intake agent reads the customer email and attached drawings.
- An engineering agent extracts materials, dimensions and processes.
- A costing agent calculates material, machining and labour costs.
- A writer agent drafts the quotation.
- A reviewer agent checks consistency; a human approves before sending.
Side-by-side comparison
| AI agent | Agentic AI system | |
|---|---|---|
| Scope | One task or job role | End-to-end workflow |
| Structure | Single agent with tools | Multiple coordinated agents |
| Autonomy | Guided, short loops | Plans and adapts over many steps |
| State | Conversation-level | Durable, resumable workflow state |
| Time to value | Weeks | Weeks to months |
| Best first step? | Yes, for most companies | After proving a few agents |
Common building blocks
- Models: GPT, Claude, Gemini or open-source models, often mixed by task.
- Tools: APIs and MCP servers that connect to your systems.
- Knowledge: retrieval (RAG) over documents and databases.
- Orchestration: frameworks such as LangGraph or agent SDKs for control flow and state.
- Guardrails and evaluation: input and output checks, test sets, tracing and cost monitoring.
Risks to manage
- Errors compound in long workflows, so add validation between steps.
- Costs can grow with loops and large contexts; set budgets and use smaller models where possible.
- Security: restrict tool permissions and require approvals for sensitive actions.
- Transparency: log decisions so humans can review and improve the system.
Start with one reliable agent that saves real hours. Then connect agents into agentic workflows once you trust the building blocks.
Where ENGIMANDI can help
We design and build AI agents, agentic AI systems and the MCP servers that connect them to your data, including specialised agents for engineering and CAD workflows. Talk to us on WhatsApp.