How AI Agents Work

An AI agent is a model that runs in a loop: it reasons, decides to call a tool, observes the result, and repeats until it has a final answer or a finished task. Chatbots produce a response. Agents produce a sequence of actions on a real system.

In plain English

An agent is like an intern with a checklist: think, do something, check the result, repeat — until the task is done. What separates a useful intern from a chaos agent is supervision. The tools, the loop, and the guardrails are the supervision.

Agents without guardrails are the reason some AI demos are still "researching" your question from last Tuesday.

The agent loop

Every agent run looks roughly the same:

1. The model reads the conversation plus a list of available tools.
2. It either produces a final answer or calls a tool with arguments.
3. The system executes the tool and returns the result.
4. The result becomes new context, and the loop continues.

What makes an agent useful is the quality and breadth of its tools. Orbit gives agents web search, file read and write, code execution inside a 1-hour cloud sandbox, database operations, image and video generation, and app-building operations like installing dependencies and running dev servers.

Two layers of planning

Good agents plan twice. Before doing heavy work, Orbit's build pipeline runs a planning phase that proposes a file manifest and phases — then a separate generator writes the code. Research agents run an even more structured loop: plan, classify, generate queries, search, extract, merge, check coverage, repeat across 8–14 rounds and 65+ sources.

Splitting planning from execution is what keeps an agent on track instead of improvising its way into a mess.

Where agents go wrong

The honest part of agent engineering is that loops fail in predictable ways:

• Runaway loops — the model keeps calling tools without converging.
• Confident garbage — the model claims it wrote a file it didn't.
• Error compounding — one failed tool call cascades into ten.
• Broken convergence — the agent "finishes" with nothing usable.

None of these are rare. That's why production agents are built with safeguards, not vibes.

The safeguards that keep agents honest

Every Orbit agent run operates under enforced limits:

• Tool ceilings — up to 25 tool calls per chat turn, 40 for deep research chat, and 220 for a build, with a soft warning near the top.
• Wall-clock timeout — every turn has a 10-minute timeout, with a short grace period before a hard stop.
• Circuit breaker — after five consecutive tool errors, tools are stripped and the agent is nudged to answer from what it has.
• Verification — file writes are confirmed against the sandbox filesystem, not taken on faith.

When a ceiling is hit, the system extracts the best answer it already produced rather than failing empty.

Parallel tools and file safety

Modern agents don't call tools one at a time. Independent calls run in parallel with Promise.allSettled, so five searches or three file reads can overlap. But tools that modify the same file are scheduled sequentially, preventing race conditions on writes. That combination — parallelism where safe, serialization where needed — is what makes agents fast without being sloppy.

What to ask about any agent

• What tools does it have, and which are real versus simulated?
• What happens when a loop exceeds its budget?
• Does it verify its own work on a real system?
• Can you see the tool calls as they happen?

If a tool can't answer those four questions, treat its output with suspicion. Agentic AI is powerful precisely because it acts — and acting without verification is how things break.

Try it and compare

Give two agents the same research-and-summarize task: one with hard limits, one without. Ask the unlimited one for a status update tomorrow. Then check Orbit's ceilings — 25 tool calls per chat turn, 220 per build, a 10-minute timeout — and ask yourself which loop you'd trust with a real project.

FAQ

Q: What is an AI agent? A: An AI agent is a model that runs in a loop: it reasons, decides to call a tool, observes the result, and repeats until it produces a final answer or completes a task.

Q: What tools do AI agents actually use? A: Common tools include web search, file read and write, code execution in a sandbox, database queries, image and video generation, and app-building operations like installing dependencies.

Q: Can an AI agent run forever? A: No — production agents enforce hard limits. Orbit allows up to 25 tool calls per chat turn, up to 220 for a build, and a 10-minute wall-clock timeout per turn.

Q: Why would an AI agent fail? A: Agents fail when the model misjudges context, tools return errors, or a loop converges on a broken approach. Safeguards — ceilings, timeouts, and circuit breakers — stop the failure from compounding.

Build it on Orbit

Orbit's agents combine a ReAct loop, parallel tool execution, and hard safeguards like the 25-call ceiling and 10-minute timeout — so you get agentic power with guardrails. Start building on Orbit.