AI agents are the most hyped concept in enterprise technology right now. Vendors promise autonomous systems that can handle complex, multi-step tasks end-to-end — from researching a market opportunity to drafting and sending a proposal. The reality, as always, is more nuanced.
This guide cuts through the noise to give you a grounded understanding of what AI agents actually are, where they create genuine value, and — crucially — when you should leave them out of your architecture entirely.
What Is an AI Agent?
An AI agent is a system that uses a large language model (LLM) as its reasoning engine to plan and execute a sequence of actions in order to complete a goal. Unlike a simple chatbot that responds to a single prompt, an agent can:
- Break a complex goal into a sequence of sub-tasks
- Use tools — search, code execution, APIs, databases — to gather information or take actions
- Evaluate the result of each step and adjust its plan accordingly
- Loop until the goal is achieved or it determines the goal cannot be met
Simple example: A user asks an agent to "research our three main competitors and produce a summary of their pricing pages." The agent searches each competitor's website, extracts relevant content, synthesises the findings, and returns a structured summary — without a human directing each individual step.
Where Agents Genuinely Excel
Agents perform best in workflows that are:
- Multi-step but structured: The task has a clear goal and a logical sequence of operations, even if the exact path varies.
- Tool-dependent: The task requires pulling data from multiple sources, running calculations, or triggering actions in external systems.
- Low-stakes per step: Individual actions within the workflow carry limited risk if they go wrong, and errors are easy to detect and correct.
- High volume: The workflow runs frequently enough that automation creates meaningful time savings at scale.
Common Failure Modes
Agents are brittle in ways that simple LLM applications are not. The most common failure modes include:
- Hallucinated tool calls: The agent attempts to use a tool or API incorrectly, producing errors that cascade through the workflow.
- Infinite loops: Without clear termination conditions, agents can get stuck attempting and re-attempting a failing sub-task.
- Context window exhaustion: Long agentic workflows accumulate context that eventually exceeds the LLM's window, degrading reasoning quality.
- Compounding errors: A mistake at step 2 of a 10-step workflow propagates and amplifies through every subsequent step.
When Not to Use Agents
Agents are the wrong choice when:
- A single well-crafted LLM prompt can solve the problem
- The workflow involves high-stakes, irreversible actions (sending emails, processing payments, updating records)
- You don't have robust logging, monitoring, and human-in-the-loop checkpoints in place
- Your team lacks the MLOps maturity to debug non-deterministic, multi-step failures
AI agents represent a genuine leap forward in automation capability — but they require careful architecture, robust guardrails, and a mature operational environment. If you're evaluating agentic AI for your organisation, talk to us before you build.
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