# Give your research agent a spending limit

> The agent checks remaining credits before each deep run and degrades to lighter tools when the budget thins.

- For: Ops / Agent builder
- Category: Agent & Workflow Automation
- MCP tools: get_credit_status, deep_query, ping
- HTML version: https://veridive.com/mcp/use-cases/budget-guarded-agent

## The problem

Autonomous agents have no native concept of money. Given a research goal and a capable toolset, an agent will happily run its most expensive tool in a loop until the goal, the budget or the month ends — and it has opinions about only the first. Everyone building agentic workflows meets this the same way: the surprisingly large usage number, discovered after the fact.

The fix is the one every metered system uses: read the meter inside the loop. An agent that checks its budget before each expensive step, and knows what to do when the answer is "thin," is an agent you can leave running — which was the entire point of building it.

## How veridive MCP helps

**get_credit_status** is the meter, free to call: balance, tier and today's remaining quota, readable by the agent mid-loop. **ping** confirms the pipe before a scheduled run burns its slot on a dead connection.

**deep_query** is the guarded spend — the pattern is a standing instruction: *check credits; above the floor, run the deep research; below it, either ask the human or degrade to lighter tools* (library search is free; summaries are cheap). Written into your agent's instructions once, the guard converts "hope the usage is fine" into designed behavior — and the same three-tool skeleton (meter, preflight, guarded spend) is the base layer under every serious agentic workflow on veridive.

## How to run it

1. **Connect veridive MCP** — One-time setup: add the veridive MCP server to Claude, ChatGPT, Cursor or any MCP client and generate your API key. Takes about two minutes.
2. **Write the guard into the agent** — The standing instruction — meter first, floor explicit, fallback named.

   ```
   Before any deep research run: check my veridive credit status. If remaining credits are under <floor>, stop and ask me instead of proceeding. Otherwise run: <research goal> — use deep_query.
   ```
3. **Add the preflight for scheduled runs** — Unattended runs get the ping first — silence should mean 'no findings', never 'no connection'.

   ```
   For the scheduled version: ping veridive first; if unreachable, notify me and skip the run.
   ```
4. **Define the degraded mode** — Below the floor, the agent still works — on the free and cheap tiers.

   ```
   When the budget is thin: skip deep_query, answer from library search and existing imports, and list what you would have researched deeply.
   ```

## Problems it solves

- Agents optimize for goals, not budgets — expensive-tool loops run to the invoice.
- Usage is discovered after the fact, as a number instead of a decision.
- Kill-switch rationing destroys the autonomy the agent was built for.
- Scheduled runs burn slots on dead connections and report nothing.

## Why it's valuable

- Leave-it-running becomes safe: the guard, not anxiety, manages the spend.
- Degraded mode keeps the agent useful below the floor — free tools still answer.
- The meter is free to read; the guard adds zero cost to the loop it protects.
- The skeleton (ping + meter + guarded spend) reuses under every agent you build next.

## Example prompts

```
Check credits; under <floor> ask me, else research: <goal> — use deep_query.
```

```
Scheduled version: ping first; unreachable → notify and skip.
```

```
Thin-budget mode: free tools only, list the deferred deep runs.
```

## FAQ

**Is the guard enforced by the platform or the prompt?**

The prompt — modern MCP clients follow check-then-decide instructions reliably. The platform's own rate and quota limits remain the hard backstop underneath.

**What's a sensible floor?**

A day's must-run work plus one unplanned deep dive. Read the meter for a week (see the know-your-burn use case) and set the floor where degraded mode is still productive.

**Does this pattern appear in the finance research desk use case?**

Same guard, team-scale framing there; this is the general agent-builder version. One pattern, every workflow.

## Related use cases

- [Run a budget-guarded research desk](https://veridive.com/mcp/use-cases/budget-guarded-research-desk.md) — Let analysts run deep research while an agent checks remaining credits before each heavy run — ambition with a spending cap.
- [Build agents that degrade gracefully on quota](https://veridive.com/mcp/use-cases/graceful-fallback-agents.md) — Teach your agent to detect low credits and fall back to free tools — library search still answers when budgets don't.
- [Know your burn across research sprints](https://veridive.com/mcp/use-cases/know-your-burn.md) — Query balance and daily quota mid-sprint — plan heavy research days around what's actually left.

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Part of the veridive MCP use-case library: https://veridive.com/mcp/use-cases.md
Server docs for agents: https://veridive.com/mcp.md