Internal orchestration
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What is AI agent? Definition and explanations
An AI agent is a system that, beyond generating a reply, plans steps, calls tools (APIs, databases, browsers) and acts toward a goal. It loops observe → decide → act under supervision.
In one sentence
An AI agent acts: it plans, calls tools and executes tasks toward a goal.
Key points
- Typical loop: goal → plan → tool calls → observation → adjust.
- Permissions (read, write, pay) define the risk boundary.
- Without guardrails, agents can repeat errors or leak data via misconfigured tools.
- Multi-step agents stay experimental for regulated flows without human sign-off.
- They differ from RPA: the path is not fully scripted upfront.
Term at a glance
- AI agent
- Intelligent agent · Autonomous AI agent
- French term
- Agent IA
- Domain
- Artificial intelligence
- Category
- Agents and automation
- Level
- Advanced
AI agents beyond one-shot chatbots
Unlike a simple chatbot, an agent can take real-world actions (ticket, email, SQL query).
Security is critical: least privilege, logging, kill switches.
Useful cases start narrow: request triage, document extraction, limited orchestration.
Designing a responsible AI agent
- 01
Limit scope
Clear mission, max duration, tool allow-list — no blanket admin access.
- 02
Log every action
Who triggered the agent, which tools, what data read or written — needed for audit and privacy law.
- 03
Human checkpoints
Client send, payment, delete: require confirmation before irreversible steps.
- 04
Test failure modes
Adversarial cases (prompt injection via inbound email) before end-user exposure.
A concrete AI agent example
An agent reads a vendor email, creates a ticket, attaches the PDF and notifies the owner — under strict authorization rules.
AI agent use cases
Operational research
Compile regulatory or technical watch with cited sources.
Augmented support
Open, enrich, and route tickets per playbooks.
Dev automation
Issues, branches, tests — mandatory human review.
Benefits and limits of AI agents
- Cuts friction between siloed apps
- Can replan when a step fails (within limits)
- Natural language for non-developers
- Larger attack surface (tools, credentials, injection)
- Token cost and latency on long chains
- Hard to guarantee without tests and observability
Why operations leaders care about AI agents
They address cross-tool to-dos that rigid scripts or isolated chatbots miss. Deploy on internal processes first with fine IAM, traceability, and privacy compliance on personal data — before customer-facing autonomy.
Frequently asked questions
Is an AI agent autonomous?
Rarely in sensible production. You set autonomy levels: suggest only, execute after click, or supervised overnight batch.
How is this different from RPA?
RPA replays fixed UI paths. Agents pick tools dynamically — more flexibility and more risk.
How to mitigate prompt injection?
Separate untrusted data (external mail) from system instructions, filter tool outputs, limit permissions (OWASP LLM Top 10).
Related terms
Sources and references
Planning an agent on your business tools? We define scope, permissions, and checkpoints before launch.
Scope an AI agent