AI Agent
A software system in which a language model selects and invokes external tools (database queries, API calls, code execution) to accomplish a multi-step task, with the model acting as the planner and the tools as the executors.
How it works
An AI agent extends a language model with the ability to take actions, not just generate text. The pattern is: define a set of tools (each with a description and a structured input schema), let the model choose which tool to invoke at each step, execute the tool, return the result to the model, and repeat until the task is complete. Common tool types include database queries, REST API calls, document retrieval, and code execution. Agents are powerful but also failure-prone: they can loop, call tools incorrectly, or fail to recognise when they have enough information to answer. Production agent systems require careful tool design, output validation, retry policies, and observability. Ayoob AI builds agent systems for workflows where the right action depends on intermediate results, but defaults to deterministic pipelines where the workflow is known in advance.
Related terms
Large Language Model (LLM)
A neural network trained on large text corpora to predict the next token given context, used for text generation, summarisation, classification, and reasoning tasks across enterprise software.
Prompt Engineering
The discipline of designing, structuring, and refining the input text passed to a language model to produce reliable, accurate, and properly-formatted output for a specific task.
Workflow Orchestration
The coordination layer that sequences AI-driven and human-driven steps in a multi-stage process, handling state, retries, error paths, and audit logging across the full workflow.
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