Introduction
At IncidentLattice, we understand the importance of incident response and postmortem analysis for AI agents. By properly documenting and learning from past incidents, engineering teams can identify patterns, improve processes, and prevent future issues. In this field manual, we will guide you through writing an effective incident postmortem for AI agents, focusing on the key aspects, reviewing the incident without blame, and turning findings into guardrails for your team.
Capture Key Details in Your AI Agent Incident Postmortem
Timeline
Start by documenting the timeline of events leading up to the incident, including the AI agent's actions and the actions of other systems involved. This will provide a clear overview of the incident's progression, which will be crucial during the review process.
Timeline
- AI agent the agent was deployed at 09:00 (UTC)
- First incident notification received at 10:15 regarding unpredictable results
- Team evaluates and confirms the agent is causing the issue (11:00)
- the agent is disabled, and the team starts investigating (11:15)
- System response time increases due to the agent removal (11:30)
- System stabilizes, and the team begins analyzing data (12:00)
- Incident resolution announced at 14:00, and the agent is re-enabled
Blast Radius
Document the impact of the incident, including any affected systems, services, or users. This information will help identify potential dependencies or side effects caused by the AI agent incident, enabling your team to proactively address these in future incidents.
Blast Radius
When developing AI agents, it is crucial to carefully consider their potential blast radius – the scope of systems and services that may be impacted by their actions.
- Systems and services directly affected by the agent's incident:
- Database system: the agent made changes to the schema, causing slow query times
- Chatbot system: the agent misconfigured its database access
- User experience: Users experienced unresponsive responses from the chatbot, leading to frustration and complaints
- Indirect impact on other AI agents:
- Anomaly Detection Agent: the agent's misconfigured database connection caused false alarms
- Predictive Maintenance Agent: the agent's changes to the system's schema impacted the predictive maintenance results
By documenting the blast radius, you can identify potential risks and dependencies early on, ensuring that you can address them effectively during development and testing stages.
Root Cause Analysis
Use the incident timeline and blast radius to identify the root cause of the incident. In this step, focus on the key decisions, configurations, or incorrect assumptions made by your team that led to the incident. This analysis will help your team learn from the incident and prevent future issues.
Root Cause Analysis
To effectively analyze the root cause, break down the incident into key aspects:
- Decisions made by the team before the incident:
- The team decided to integrate the agent with the chatbot system, leading to an increased false alarm rate
- The team assumed that the new data schema changes would have no impact on other AI agents
- Configurations that may have led to the incident:
- Team's misconfiguration of the agent's database connection parameters
- Ignoring the potential impact on other AI agents due to the team's assumption
- Incorrect assumptions made during incident resolution:
- The team assumed that the agent's incident resolution would not have an impact on the chatbot system performance
- The team incorrectly identified the root cause as the agent's database connection issues
By examining these aspects, you can gain insights into the root causes of the incident and identify potential opportunities for improvement in your AI agent development process.
Findings and Lessons Learned
In this section, summarize the findings of your investigation and the lessons your team can take away from the incident. This will help ensure that similar incidents are mitigated in the future.