# Case Study: Budgeting a Research Agent — Agent Loops, Stop Conditions and Token Budgets

Source: https://www.geekswithgeeks.com/en/agent-loops/wrap-budget-design

> Design limits and budgets for an agent that searches the web and writes a report.

## Reasoning from the task

A research agent searches, reads pages and writes a 1-page report. Searches are cheap but page text is large, so **clip every page to about 4,000 tokens**. Allow **20 steps**, **150,000 total tokens** and **3 minutes**. Stop early on a repeated search. At 90% of any limit, make a tool-free **wrap-up call** that reports what was found. Save a checkpoint after each step and log one metrics record per run.

## A safe loop end to end

Combine limits, repeat detection, context control, graceful endings and metrics into one design.

![Four layers around the loop core.](assets/figures/agent-loops/section-8-map.svg) — Figure 8.1 — Limits, guards, context control and reporting around the loop.

## The design on one page

Each line maps to a section of this course. Tune the numbers from real runs.

```text
Limits       20 steps, 150k tokens, 180 s, $1.00        (Sec 2)
Guards       RepeatGuard, 3 consecutive tool failures     (Sec 2, 5)
Context      clip pages to ~4k tokens, summarise at 60%   (Sec 3, 4)
Caching      stable system prompt + tool defs first       (Sec 3)
Ending       wrap-up call at 90%, handover on failure     (Sec 6)
Visibility   one JSON record per run, stop reason logged  (Sec 7)
```

**Quiz:** Why clip each fetched page in the research agent?

- [ ] Pages are never useful
- [x] Large page text is resent every turn and inflates cost
- [ ] The API forbids pages
- [ ] Clipping speeds up the network

*Answer:* Large page text is resent every turn and inflates cost. Big tool results are resent on every later turn, so bounding them controls total cost.