Zia Agents are genuinely powerful, but like any autonomous system, they can occasionally behave in ways that seem confusing from the outside, looping on the same action, escalating unexpectedly, or stopping without explanation. In almost every case, this comes down to one of a handful of well-understood causes: ambiguous stopping conditions, missing execution limits, or error-handling logic that isn't quite configured the way you expect. This article pulls together our detailed guides on each of these areas, so you can work through them systematically rather than guessing.

Why Agents Misbehave in the First Place

Most "strange" agent behaviour isn't actually random. It's usually the predictable result of a prompt or configuration that leaves too much room for interpretation around a specific question: what should the agent do when it isn't sure, when a tool fails, or when it has genuinely finished? Left unanswered, the agent's own reasoning tends to fill that gap in ways that look erratic from the outside, retrying a call that's never going to succeed, bouncing a ticket back and forth, or continuing to run when it should have stopped. Each of the five topics below addresses one specific version of this gap.

The Five Failure Modes

1. "My agent keeps repeating the same search or action"

If a Zia Agent calls the same tool, such as a CRM search or a DNS lookup, over and over with identical parameters, this is a Redundant Tool Call Loop. It's almost always caused by unclear stopping conditions or retry-style instructions that were never explicitly capped to one attempt.

2. "My agent keeps bouncing a Zoho Desk ticket back and forth"

If a ticket cycles between a Zia Agent and a human queue without settling, this is a ticket assignment loop, usually caused by ambiguous auto-assignment routing that lets an un-assigned ticket get re-picked-up by Zia instead of landing cleanly with a human.

3. "My agent escalated to a human, gave up, or sent a generic fallback message"

This is the Fallback Branch (also called the Failsafe Exit Branch) doing exactly what it's designed to do. It's the mechanism that catches low-confidence queries, failed API or tool calls, guardrail violations, and missing knowledge context, and routes them somewhere sensible rather than letting the agent hallucinate or hang. If this is firing more often than expected, the trigger conditions and logging in this branch are the first place to look.

4. "My agent's query failed, or it's hitting a limit I didn't set"

Zia Agents operate under a set of platform-level execution rules that aren't always obvious until you hit them: strict COQL query construction rules, a 3-attempt self-correction cap on failed queries, one-action-per-rule limits inside workflows, a 1,000 execution per day organisation-wide cap, and Session Key isolation on triggers. If an agent's behaviour seems to be capped or restricted for no obvious reason, this is usually why.

5. "I want to make my agent stop more predictably"

This is the preventive counterpart to failure mode 1. Explicit termination flags, stop sequences, and exit conditions are what you build into an agent's workflow and prompts up front, so it halts deterministically on task completion, a guardrail hit, or a handoff, rather than relying on the model to infer when it should stop.

A Practical Debugging Checklist

When an agent is behaving unexpectedly, working through these steps in order tends to surface the cause quickly:

1. Check the Observability tab first. The session trace, execution steps, and token path will usually show you exactly where the agent's behaviour diverged from what you expected, before you need to guess at a cause.
2. Look for a repeating pattern. If the same tool call or action appears more than once with identical parameters, you're looking at a loop. Start with the Redundant Tool Call Loop guide.
3. Check whether the Fallback Branch fired. If the agent escalated, gave a generic response, or handed off, check the trigger conditions on the Fallback Branch to confirm it fired for the reason you'd expect, rather than prematurely.
4. Check execution limits. If something stopped working part-way through, or a query kept failing, confirm you haven't hit a platform-level rule, such as the COQL retry cap or a workflow action limit.
5. Review your stopping conditions. If none of the above explains it, the underlying fix is usually to make the agent's termination logic more explicit in its prompt or workflow, rather than relying on the model to infer when it's done.