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How do AI agents work?

Quick Answer

AI agents are autonomous systems that can plan, reason, and execute multi-step tasks by combining a large language model with tools, memory, and decision-making loops. Unlike simple chatbots that respond to single prompts, agents break complex goals into subtasks, use external tools like APIs and databases, evaluate their own progress, and iterate until the task is complete.

Summary

Key takeaways

  • Agents combine LLMs with tools, memory, and planning capabilities
  • They can execute multi-step workflows autonomously with minimal human input
  • Agents use reasoning loops to evaluate progress and adjust their approach
  • Business applications include research, data analysis, and process automation

Core Components of AI Agent Architecture

An AI agent consists of several interacting components. The language model acts as the reasoning engine, interpreting instructions and deciding what actions to take. A tool layer provides the agent with capabilities beyond text generation: calling APIs, querying databases, searching the web, reading files, or interacting with other software systems. A memory system, both short-term (conversation context) and long-term (persistent knowledge store), allows the agent to maintain context across multiple steps. A planning module breaks complex goals into manageable subtasks and determines the optimal sequence of actions. An evaluation component assesses whether each step was successful and whether the overall goal has been achieved, enabling the agent to retry, adjust, or escalate when needed.

Business Applications of AI Agents

AI agents are transforming business operations by automating workflows that previously required human judgement at each step. In customer service, agents can handle complex enquiries that span multiple systems: checking order status, processing refunds, and updating records in a single interaction. In research and analysis, agents can gather information from multiple sources, synthesise findings, and produce structured reports. In operations, agents can monitor systems, detect anomalies, investigate root causes, and take corrective action. In procurement, agents can compare supplier quotes, check compliance requirements, and prepare purchase orders. The key advantage is that agents handle the end-to-end process rather than just individual steps, reducing handoffs and processing time.

Safety and Control Considerations

Deploying AI agents in business settings requires careful attention to safety and control. Implement guardrails that limit what actions an agent can take, particularly for irreversible operations like sending emails, making payments, or modifying records. Use human-in-the-loop checkpoints for high-stakes decisions. Monitor agent behaviour through logging and observability tools that track every action taken. Set resource limits to prevent runaway processes. Start with narrow, well-defined use cases and expand scope gradually as you build confidence in the system's reliability. Always maintain the ability for humans to override, pause, or stop agent operations when needed.

Related

Related questions

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FAQ

Frequently asked questions

AI agents are increasingly reliable for well-defined, bounded tasks. Production deployments should include guardrails, monitoring, and human oversight. Start with lower-risk use cases and expand as you validate reliability in your specific context.

Costs depend on the complexity of tasks and the models used. A typical agent workflow processing a complex request might cost £0.05 to £0.50 in API calls. High-volume deployments benefit from smaller, fine-tuned models or local deployment to reduce per-task costs.

RPA follows rigid, predefined rules and breaks when processes change. AI agents use language models to understand context, make judgement calls, and adapt to variations. Agents handle unstructured data and ambiguous situations that RPA cannot.

Implement confidence thresholds and escalation rules. When an agent encounters ambiguity, reaches a predefined decision boundary, or its confidence score falls below threshold, it should pause and request human input. Design clear escalation paths for different types of uncertainty.

Python is the most common language for AI agent development, with frameworks like LangChain, AutoGen, and CrewAI. TypeScript/JavaScript is increasingly used with frameworks like Vercel AI SDK. The choice depends on your existing technology stack and team expertise.

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