What is an AI Agent?

How an AI agent works

A production agent runs a loop, often built on frameworks like LangGraph, CrewAI, or the Claude Agent SDK:

1. Plan. The model reads the current state (task, prior steps, available tools) and decides what to do next.
2. Act. It calls a tool: search, code execution, an MCP server, an API.
3. Observe. The tool returns a result, which is appended to the context window.
4. Reason. The model interprets the result, updates its plan, and either calls another tool or produces a final answer.

The loop continues until the agent decides the task is complete (or hits a step limit, a cost cap, or a timeout). Inside the loop, the agent’s context window accumulates tool calls, results, and reasoning traces. Managing that accumulation is most of the engineering work.

Why most agent failures are context failures

When an agent produces a wrong answer, the instinct is to rewrite the prompt. The data suggests that’s usually the wrong fix. Three context problems drive most production failures:

• Missing information. The agent confidently proceeds without knowing something relevant. Retrieval missed it, the tool didn’t expose it, or the agent never thought to ask.
• Stale information. The agent works from data that was once accurate but is no longer. Training cutoffs, cached retrievals, and slow re-indexing all contribute.
• Overloaded information. Tool outputs, conversation history, and retrieved documents accumulate until the model can’t attend to all of it. Accuracy degrades even when the answer is somewhere in the context.

This is why context engineering has emerged as the discipline around agents. You can’t phrase your way out of missing data or truncated memory.

Common misconceptions about AI agents

• “More capable models make agents reliable.” More capable models raise the ceiling on reasoning, but agent reliability is gated by context quality. Tau-Bench results show even frontier models complete only 16% of complex multi-step tool tasks.
• “An agent is just a chatbot with tools.” The loop changes the problem entirely. A chatbot fails on one bad response; an agent can cascade errors across a dozen steps before the human notices.
• “Agents should have broad access so they can handle anything.” 88% of organizations report AI agent security incidents (Gravitee, 2026). Broad default access is the common root cause. Scoping context per task is both a security and a quality practice.
• “Multi-agent systems are strictly better than single agents.” They’re better at specialization and parallelism but worse at context coherence. Most multi-agent failures are handoff failures, not reasoning failures.

AI agents and Wire

Agents need context, and they need it scoped per task. Wire containers are the retrieval half of that: upload files or have agents write entries directly, and each container exposes MCP tools (wire_explore, wire_search, wire_write, wire_delete, wire_analyze) that an agent can call mid-loop. Containers are private by default, scoped per organization, and shareable when you want teams or agents to pool context. The agent keeps the reasoning role; Wire keeps the information accessible, structured, and current.