| Agent & Tool-Based Automation Workflow – Using Retrieval & Generation | |
| In modern automation systems, agents and tools work in tandem to bridge the gap between user intent and actionable workflows. An agent is the | |
| orchestrator—it interprets natural language requests, selects the appropriate tools, and supervises the execution of subtasks. Tools are the | |
| specialised components the agent invokes: for retrieval of knowledge, execution of code, external API calls, or summarisation of results. | |
| By separating these concerns, you build a flexible system: the agent handles reasoning and decision-making, while tools execute concrete operations. | |
| Retrieval plays a pivotal role in this architecture. Before generation, the system identifies relevant context or data—perhaps past automation scripts, | |
| documentation, or process logs—using a retriever tool. That context is fed into the generation stage, where the agent or model crafts a response or | |
| action plan grounded in the retrieved evidence. This Retrieval-Augmented Generation (RAG) pattern ensures that the system doesn’t hallucinate its | |
| way to an answer but bases its output on real, indexed information. | |
| Tool invocation adds another dimension of operational power. Once the agent decides “I need this tool”, it passes structured inputs to the tool, | |
| receives a result, and continues its reasoning with that result. For example: user says “Create a script that uploads the latest financial report.” | |
| The agent retrieves the report template, invokes a code-generation tool, reviews the generated script via another tool, and then executes or delivers | |
| the final output. Each step is visible, modular, and auditable. | |
| Finally, good workflows enforce guardrails, feedback loops and evaluation. The agent should know when to escalate to a human, how to log tool usage | |
| (for observability), and how to evaluate whether the output meets criteria (accuracy, relevance, correctness). Embedding these practices from the | |
| start allows the system to evolve—improving over time, scaling across domains, and adapting to new use-cases without breaking. |