By SpiderHunts Technologies · · 10 min read
"AI agent" is the most over-used phrase in tech right now: but the real thing is genuinely powerful when built well. We design and ship AI agents with Claude for clients across the USA, UK, Canada, Europe and South Africa, and the engineering is more disciplined than the hype suggests. Here's how it actually works.
What an AI agent really is
An agent is software that pursues a goal: it reasons about what to do, takes actions in external systems through tools, observes the results, and adapts: without a human directing every step. The difference from a chatbot is action. A chatbot answers "what's my order status?"; an agent looks it up, checks the courier, and updates the ticket.
The building block: tool use
A tool is a function you expose to Claude with a name, a clear description, and a typed input schema. The description is critical: it's what the model reads to decide when to call the tool, so be prescriptive ("Call this when the user asks about an order's status"). When Claude wants a tool, it returns a structured tool request; your code runs the function and returns the result.
The agentic loop
An agent is just tool use in a loop. Claude decides on a tool, your code executes it, the result goes back, and Claude decides the next step: repeating until it reaches the goal. The Anthropic SDKs ship a tool runner that handles this loop automatically; for fine-grained control (human-in-the-loop approval, custom logging, conditional execution) you can write the loop manually. Adaptive thinking lets the model reason between steps.
Where MCP and managed agents fit
You can give an agent custom tools directly, or connect it to MCP servers so tools are standardised and reusable across many agents. For long-running, stateful agents, Anthropic's managed agents run the loop on Anthropic's side and provision a sandboxed container where the agent's tools (bash, file ops, code) execute: with credentials kept in vaults, never in the model's context.
Should you build an agent? Four checks
Before reaching for the agent tier, confirm all four: Complexity: is the task multi-step and hard to fully specify in advance? Value: does the outcome justify higher cost and latency? Viability: is the model genuinely capable at this task? Cost of error: can mistakes be caught and recovered (tests, review, rollback)? If any answer is "no", stay at a simpler tier: a single call or a code-controlled workflow.
Designing a reliable agent
Keep the tool surface focused and well-described. Gate destructive actions behind approval. Validate tool inputs. Manage context over long runs (compaction and context editing keep the transcript lean). And measure: track tool-call accuracy, completion rate, and cost per task. A well-scoped agent that does five things reliably beats a "do anything" agent that does twenty things unpredictably. This is the backbone of real business automation.