What Is AI Agent?
Also known as: Autonomous Agent, LLM Agent
Definition
An AI agent is a system that wraps a language model with tools, memory, and a goal so it can plan a sequence of steps, call external systems, observe the results, and iterate until the objective is met or a limit is reached.
AI Agent Explained
The distinguishing mechanism is the loop. A plain assistant maps one prompt to one response. An agent receives an objective, decides which tool to call, executes it, reads the result, and decides the next step, repeating until it judges the task complete or hits a bound on steps, time, or cost. Tools are ordinary functions the model can request by name with structured arguments: query an ERP table, look up a part, create a record, send a message.
Reliability degrades multiplicatively with step count, and this is the central engineering reality. A step that succeeds 95 percent of the time gives roughly 60 percent success over ten steps and about 36 percent over twenty. Long autonomous chains therefore fail far more often than single-step performance suggests. Effective designs keep chains short, make each step independently verifiable, and checkpoint state so a failure resumes rather than restarting.
In ERP environments the highest-value agents are narrow and bounded. Reconciling a supplier invoice against a purchase order and receipt, triaging an inbound RFQ and drafting a response for review, monitoring a shortage report and proposing expedites, or investigating a scheduling exception and assembling the evidence a planner needs. Each has a clear objective, a small tool set, and a natural point where a human takes over.
Authorization is where agent projects most often stall at security review, and correctly so. An agent that can write to the ERP is a program acting under some identity, and every write must be evaluated against the entitlements of the person who initiated the task, not a service account. Add step limits, cost limits, full action logging, and human approval gates on consequential operations. Start with read-only agents that propose actions, and grant write capability only after the proposal quality is proven in production.
Why It Matters
- Multi-step automation reaches work that single-turn assistants cannot touch, such as reconciliation and exception investigation across systems.
- Compounding step failure means a chain that looks reliable per step can fail most of the time end to end, forcing short bounded designs.
- Write access to ERP makes authorization design and approval gates a security prerequisite rather than a later hardening exercise.
- Read-only proposal agents deliver much of the value at a fraction of the risk, making them the right first production deployment.
In Practice
A useful pattern: run the agent in proposal mode for a full quarter. It performs the whole investigation and drafts the ERP transaction but does not commit it. Measure how often a human accepts the proposal unchanged. When acceptance is consistently high, enable write access for the specific narrow case that earned it, not for the agent generally.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot answers within a conversation and takes no action outside it. An agent is given a goal and a set of tools, then plans and executes multiple steps against real systems, observing results and adjusting as it goes. The practical difference is consequence: an agent can change data, so it needs authorization, approval gates, and audit logging that a chatbot does not.
Should AI agents be allowed to write to the ERP?
Eventually, for narrow proven cases, with controls. Start read-only, having the agent propose transactions a human reviews and commits. Track acceptance rate over a meaningful period. Grant write access only for the specific operation whose proposals are consistently accepted, scoped to the initiating user's permissions, with full logging and an approval gate on anything financially or contractually significant.
Related Terms
MCP (Model Context Protocol)
MCP (Model Context Protocol) is an open standard that defines how AI applications connect to external tools and data sources, so any compliant client can use any compliant server without custom integration code for each pair.
AI Guardrails
AI guardrails are the technical and policy controls placed around an AI system that constrain what it may receive as input, what it may produce as output, and what actions it is permitted to take, independent of what the model itself decides.
Prompt Injection
Prompt injection is an attack in which instructions are hidden inside content an AI system reads, causing the model to follow the attacker's directions instead of the operator's, because a language model cannot reliably separate instructions from data.
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