What is agentic AI?
Agentic AI is artificial intelligence that pursues a goal through a sequence of actions rather than producing a single answer. Given an objective such as “reconcile this week’s supplier invoices against purchase orders,” an agentic system breaks the work into steps, calls tools (a database query, an ERP API, a document reader), checks the results, and decides what to do next until the goal is met or it needs a person. The individual programs that do this are AI agents; agentic AI is the broader approach and the class of systems built on it.
The technology became practical when large language models learned to use tools reliably. The model reads the goal and the current state, chooses a tool and its arguments, receives the tool’s output as text, and reasons about the next step. This loop, often called the ReAct pattern, repeats until the model declares the task complete. Memory, planning modules, and approval checkpoints are added around the loop to make it dependable.
How agentic AI works
A production agentic system has five parts. The model supplies reasoning and language understanding. Tools expose business systems as functions the model may call, increasingly through standards such as the Model Context Protocol. An agent runtime executes the loop, enforces limits on steps and spending, and records every call. Memory holds the state of the current task and, in some designs, lessons from earlier tasks. Governance controls decide which tools the agent may use, under which identity, and which actions require a human to approve before they execute.
Multi-agent designs split work among specialized agents, with an orchestrator agent assigning subtasks and assembling results. This helps on long tasks, but each additional agent adds cost, latency, and places where errors can compound.
Agentic AI compared with generative AI
| Aspect | Generative AI assistant | Agentic AI |
|---|---|---|
| Output | Text, code, or an image for a person to use | Completed actions in business systems |
| Interaction | One prompt, one response | Many steps with tool calls between them |
| Access needed | Documents and context in the prompt | Credentials for APIs, databases, and applications |
| Main failure mode | An incorrect answer a person can reject | An incorrect action that changes a record |
| Control required | Review of the output | Scoped permissions, approvals, and audit logs |
Agentic AI in practice
A building products distributor runs an agent on its order desk. When a customer email arrives, the agent identifies the account, extracts the requested items, checks stock and contract pricing in the ERP, and drafts an order. Orders under a value threshold with exact catalog matches are entered automatically. Anything else, including substitutions and credit holds, is presented to a representative with the agent’s reasoning attached. A managed service provider uses a similar design for ticket triage: the agent gathers device logs, proposes a fix from the runbook library, and applies it only after a technician approves.
Agentic AI and private AI
Agents need broad access to internal systems to be useful, which makes the hosting decision more consequential than it is for a chatbot. Running the model and runtime on infrastructure the company controls keeps credentials, customer records, and the full action log inside its own boundary. In Kirk Tech Solutions engagements, every agent runs under its own identity with least-privilege access, and any action that writes to a system of record passes through a human approval step. FlatClaw, the open-source private AI platform from Kirk Tech Solutions, is built around that model of per-user agents with approvals and audit logs.
What to watch for
- Map every tool the agent can call and classify each one as read-only or as a write to a system of record.
- Require an approval step for writes until the agent has a measured track record, and keep it for high-value actions permanently.
- Set hard limits on steps, time, and spend per task, because a looping agent can run up costs quickly.
- Log every prompt, tool call, and result so that an incorrect action can be traced and reversed.
- Start with a narrow, high-volume task that has a clear definition of done, and expand scope only after the first deployment is stable.