An AI agent is a system that uses a model to choose actions in pursuit of a goal, observes the results and can decide what to do next. Tools connect that decision process to search, files, databases and other services.
A fixed workflow can use the same tools while keeping the sequence under application control. The distinction matters because model directed actions introduce more possible execution paths to evaluate.
Tools connect the model to external capabilities
A tool can retrieve information, perform a calculation, transform a file or request a change in another system. The surrounding software executes the operation and returns its result.
A document assistant might search a policy collection and fetch individual passages. An order assistant might look up a shipment and, when authorised, request a cancellation. A coding agent might inspect files and run tests in a bounded workspace.
Existing models can often use such interfaces without custom training. They still need evaluation on the actual tools and tasks. A prebuilt integration does not establish that the assembled product is reliable.
Functions, extensions, data stores and plugins
These terms describe integration arrangements, and their meaning varies across platforms.
Functions expose named operations with arguments. Extensions connect capabilities through a platform defined interface. Data stores provide retrieval over stored information. Plugins package integrations, configuration or instructions, sometimes including tools.
The names alone do not establish who holds credentials or whether an operation can write data. Inspect the actual execution path and permission scope. A data store integration can expose sensitive records even if it never modifies them.
The Model Context Protocol provides a client server architecture for connecting applications to capabilities such as tools and resources. MCP is an integration protocol, not a guarantee that a tool is trustworthy, and plugins are not universally MCP servers.
Separate read access from action authority
Read tools can reveal private information or support incorrect conclusions. Write tools can additionally change records, send messages or incur costs.
Create a tool inventory with the data accessed, possible effects, reversibility and authorisation method. A useful entry is more specific than “CRM access” because it identifies which records can be read and which fields can be changed.
Keep credentials in the execution layer. Scope access to the user, task and environment. A model's interpretation of a retrieved instruction must not widen those permissions.
Choose an agent only when dynamic decisions help
A stable process with known branches may work well as a conventional workflow. Agentic control is useful when the next step depends on observations that cannot be fully enumerated in advance.
For example, investigating a build failure may require choosing which files and tests to inspect after each result. Sending a standard receipt usually needs no such autonomy.
Start with a clear completion condition and a limited tool set. Add capabilities in response to evaluated task needs. Model quality, context quality and tool design all affect the result.
Assess the system through its actions
An agent's final explanation can sound successful while the requested change never happened. Verify outcomes through the underlying system. Check whether the file exists, the test passed or the authorised record reached the intended state.
Also inspect unnecessary or forbidden actions along the way. A correct final answer does not excuse an unauthorised data read.
Continue with the agent loop to see how observations, budgets and stop conditions fit together. The tool list defines available operations; the loop and execution controls determine how those operations are used.