AI Agent Workflows – An Introduction & Best Practices

Modern AI agents are more than chatbots—they are systems capable of autonomously performing tasks, connecting to tools, retrieving knowledge, and making
decisions. At their core, an agent takes input (a user request), reasons about what to do (which tools to call or which data to fetch), executes 
operations, and returns output. This workflow involves: interpreting intent → selecting action → retrieving context/data → executing tools/commands → 
generating a response or action. By mapping out this sequence clearly, you build agents that are predictable and effective.

One of the most important steps in agent workflows is retrieval of context. When an agent is asked a question or given a task, it often needs data 
beyond its training—such as documents, APIs, logs or databases. A retrieval component finds relevant pieces of evidence, which are then used by the 
generation or tool-execution stage. Without such context, the agent risks hallucinating or producing irrelevant output.

Equally important is tool invocation and orchestration. Once the agent has retrieved relevant context, it may decide to call a tool—for example, 
running a script, accessing a database, invoking an API, or performing a calculation. The workflow must clearly define how tools are selected, how 
inputs are formed, how outputs are handled, and how errors or unexpected results are managed. Modularising tools and defining interfaces make this 
step robust and maintainable.

Finally, building production-worthy agent workflows demands governance, observability and iterative improvement. Agents should log their decisions 
(which tool was called, what data was retrieved), monitor performance (latency, accuracy, failures) and allow for human intervention when needed. 
Best practice also says to start with narrow-scoped tasks (one reliable function) and expand gradually, ensuring each stage works before scaling. 
With these practices, your agent workflows become both reliable and extendable.