What are we defending against? In this case, the adversary is opacity. A common capability gap for new members is the inability to audit an agent's operational chain-of-custody, leading to undetected prompt leaks, unintended tool executions, or privilege escalations within your own configured environment. Your question indicates you've moved beyond theoretical threat models and are now confronting the practical attack surface of a live system.

For a foundational review, you must instrument your agent to produce an actionable audit trail. This is not merely about reading logs; it's about structuring them to answer three core adversarial questions:
* Was the agent's intent (as derived from the initial user input) preserved throughout the execution chain, or was there a divergence due to context window limitations or intermediate parsing errors?
* Did all tool calls and their arguments fall within the expected parameters of the allowed action policy for that specific session's authorization level?
* Was any part of the original prompt, system instructions, or retrieved context inadvertently leaked into the final, external-facing output?

Start by enabling the most verbose logging level your agent framework provides (e.g., LangChain's debug mode, AutoGen's logging to file). Do not rely on console output alone. Your immediate goal is to capture the complete sequence, which typically follows this attack tree branch:
1. **Input Ingestion & Parsing:** Log the raw user query and the fully resolved system prompt context. Look for injection points.
2. **Planning & Reasoning Steps:** If using ReAct or Chain-of-Thought, log each internal reasoning step. This is your first line of defense for detecting logic corruption.
3. **Tool/Function Selection:** Log the exact API or function name called, with the full arguments payload. Map this against your allowed list.
4. **Tool Execution Result:** Log the raw result returned from the tool (database query, API response, code execution output). This is where data exfiltration or unexpected states can be introduced.
5. **Synthesis & Output Generation:** Log the final assembly of tool results into the natural language response. Scrutinize for context bleed or hallucinations that could contain sensitive data.

I recommend a structured review process for your first few audits. Create a simple matrix with the following columns: `Step Number`, `Actor (User/Agent/Tool)`, `Action/Event`, `Data Payload (Sanitized)`, `Observed Deviation from Policy`, and `Mitigation Hypothesis`. Populate this matrix from your logs. The act of categorization will reveal patterns and gaps in your monitoring.

Your next step after establishing basic logging is to introduce adversarial examples into your test queries. Purposefully craft inputs designed to cause confusion—ambiguous requests, multi-step instructions that could bypass a step-level permission check, or prompts that ask the agent to "forget" its initial instructions. Observe how your agent's actual actions, as recorded in the logs, handle these edge cases. This will directly highlight where your current safeguards are insufficient and where you need to implement additional validation or sanitization controls.