Lesson 1695 of 2116
Agentic AI: loop budgets that prevent runaway agents
Cap the agent on steps, tokens, dollars, and wall-clock. Without budgets, a confused agent burns money until it hits a quota you didn't set.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2loop budgets
- 3cost control
- 4termination
Concept cluster
Terms to connect while reading
Section 1
The premise
An agent that doesn't know how to stop will retry, replan, and spiral. Hard budgets — step count, token spend, time, and dollars — give you a floor on incidents.
What AI does well here
- Plan within a stated step limit when you tell it the budget
- Summarize progress when asked to compact context
- Stop when a tool returns a terminal signal
What AI cannot do
- Self-impose budgets without enforcement in code
- Accurately estimate remaining work mid-task
- Recover the spend from a runaway loop after the fact
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “Agentic AI: loop budgets that prevent runaway agents”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 22 min
Provider Routing: Switch Models Without Rewriting the App
Build a small model router that can send easy, private, or expensive tasks to the right model family.
Creators · 40 min
Setting Context-Window Budget Policies for Long-Running Agents
How to keep an agent's context window from filling with noise mid-run.
Creators · 48 min
Computer Use API: Letting AI Click Through GUIs
Computer Use lets Claude see your screen and use it — mouse, keyboard, apps. The capability is real, the gotchas are real. A hands-on look at what works in 2026.