How to prevent agent hallucinations and failures: seven safeguards before launch
A financial compliance AI agent fabricated a non-existent FSC regulatory memo reference, regulation number, date, content, nine days after launch, and the compliance officer nearly approved it. Agent failures don't come from weak models. The problem is the lack of safeguards between the agent and reality. This article covers seven safeguards to implement before launch.
By
Tenten AI 研究團隊
應用 AI
Published
February 7, 2026
Read time
5 分鐘
Last quarter we took over a financial compliance AI agent for back-office regulatory review. It reads contracts, compares them to regulations, and generates compliance summaries. The demo seemed fine. On day nine in production, it inserted a fake FSC regulatory memo reference into an M&A document, regulation number, date, content, all made up. The compliance officer was about to approve it.
The model wasn't the problem. No one had put safeguards between the agent and reality.
Agent failures rarely happen all at once. More often it's a series of small unchecked deviations: a made-up reference, a misplaced parameter, a tool call stuck in an infinite loop. Fixing this doesn't mean using a stronger model. It means adding seven safeguards before launch.
Seven lines of defense against agent hallucinations and failures
| # | Safeguard | What It Blocks | Real-World Incident |
|---|---|---|---|
| 1 | Grounding | Unsourced assertions | The fabricated FSC memo above |
| 2 | Forced tool use | Guessing when it should query | Agent quoting a price from memory instead of calling the pricing API |
| 3 | Output validation | Out-of-bounds values and formats | Negative refund amounts getting processed anyway |
| 4 | Stop-loss mechanisms | Infinite loops and runaway execution | Retrying the same step 40 times and burning through API quota |
| 5 | Minimal permissions | Unauthorized execution | Customer service agent able to accidentally delete orders |
| 6 | Human-in-the-loop | High-risk, irreversible actions | Auto-sending a poorly worded apology to a major customer |
| 7 | Monitoring and audit trails | Post-incident root cause analysis failures | Still unable to determine root cause three days later |
The first four: blocking hallucinations before output
Grounding. Every factual statement must link back to a traceable source. No source means don't output it, return 'I can't confirm that.' This prevents hallucination. We added forced citations to the compliance agent: any claim not traceable to the actual statute got flagged red. The hallucination rate dropped significantly.
Forced tool use. The model defaults to 'I think it's approximately...' For any real-time data, inventory levels, pricing, account balances, require a tool call first. Get the actual result before answering. Skip the approximations.
Output validation. Validate with schemas, not just natural language. Amounts, dates, and status codes go through structured format with rules: refunds can't be negative, dates can't predate today, status values must be on the whitelist. A negative refund gets caught here, not at accounting.
Stop-loss mechanisms. Set hard limits on each agent: maximum tool calls per task, maximum iterations, maximum tokens. When limits are hit, stop execution, alert, and escalate to a human. A circuit breaker limiting retries to three would have stopped the loop before burning through API quota.
The last three: minimal permissions, humans in the loop, preserving visibility
The first four prevent saying the wrong thing. The last three prevent doing the wrong thing and prevent being unable to figure out what went wrong.
Minimal permissions. Each agent should only have the tools it needs for that specific task. A customer service agent doesn't need delete-order access. Read and status-update access are sufficient. Destructive actions should either be forbidden or require a second approval.
Human-in-the-loop. Not everything needs human review, but high-risk irreversible actions do, sending emails, moving funds, public announcements. You need a clear line between what to block and what to allow. Otherwise review fatigue makes approval automatic and defeats the point.
Monitoring and audit trails. Log every tool call, prompt, and intermediate output with complete traceability. Track continuous metrics: grounding success rate, tool accuracy, violations blocked. Value comes from replaying what happened within three minutes when something goes wrong, not reverse-engineering it three days later after a customer escalation.
None of these seven safeguards come from the model. All are built at the system level. Before launch, go through the checklist: grounding, forced tools, output validation, stop-loss, minimal permissions, human review, monitoring. Don't skip any. A polished demo is not an acceptance criterion. Success means the agent runs after launch without breaking things.
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