Postmortem

A chronological reconstruction of the incident, from the first triggering event to full resolution. This is the foundational artifact, built from system logs, metrics, and human accounts. It establishes a shared, objective narrative.

• Key elements: Precise timestamps (UTC), state changes, detection points, and remediation actions.
• Purpose: Eliminates speculation, aligns the team on the sequence of failures, and is essential for identifying causal chains and latent conditions that contributed to the outage.

The systematic process of identifying the fundamental, underlying cause(s) of an incident, rather than its symptoms. Effective RCA moves beyond the proximate cause (e.g., 'server crashed') to systemic factors.

• Techniques: The 5 Whys, Fishbone (Ishikawa) diagrams, and fault tree analysis.
• Goal: To find the point in the process or system where an intervention could have prevented the failure or reduced its impact. This shifts focus from individual error to process and design flaws.

A quantitative and qualitative measurement of the incident's consequences. This grounds the analysis in business reality and prioritizes follow-up actions.

• Metrics to capture: User-facing error rates, increased latency, downtime duration, affected revenue, customer support ticket volume, and reputational damage.
• Use Case: Provides the data needed to calculate the incident's cost, validate the severity level, and justify investment in preventative measures. It directly informs the error budget consumption for the service.

A concrete, assigned list of tasks generated from the RCA to prevent recurrence and improve system resilience. This is the primary output that closes the feedback loop.

• SMART Criteria: Each item should be Specific, Measurable, Achievable, Relevant, and Time-bound.
• Categories: Actions often fall into: bug fixes, improved monitoring/alerting, documentation updates, process changes (e.g., deployment checklists), and architectural improvements. Ownership and deadlines are non-negotiable.

The foundational psychological safety required for an effective postmortem. The goal is to improve systems, not assign blame to individuals. Human error is treated as a symptom of deeper systemic problems.

• Manifestation: Language focuses on 'what' and 'how,' not 'who.' Discussions assume everyone involved was acting with the best available information and intentions.
• Outcome: Encourages full transparency, honest disclosure of mistakes, and collaborative problem-solving. This is critical for uncovering the true root causes that often involve complex interactions between people, processes, and technology.

The process of disseminating the lessons learned from the postmortem beyond the immediate incident response team. This amplifies the value of the analysis and prevents siloed knowledge.

• Methods: Publishing the postmortem document (often with sensitive details redacted) in an internal wiki, holding brief read-out meetings, and integrating findings into onboarding or training materials.
• Benefit: Turns a local incident into an organizational learning opportunity, building institutional memory and raising the mean time to innocence for future, similar issues across all engineering teams.

A method for profiling requests as they propagate through a distributed system. In a multi-agent context, it captures the spans representing each agent's processing step, their parent-child relationships, and timing data.

• Core Purpose: To reconstruct the precise execution path and latency of a workflow across agents.
• Postmortem Value: Provides the factual timeline needed to identify the specific agent, message, or dependency where a failure or bottleneck originated, moving analysis beyond speculation.

A target level of reliability or performance for a service, defined as a percentage over a time period (e.g., 99.9% availability per month).

• Quantifies Impact: The business impact of an incident is measured by the error budget consumed—the amount of unreliability tolerated.
• Postmortem Trigger: A significant breach of an SLO, consuming a large portion of the error budget, is a common catalyst for conducting a formal postmortem to prevent recurrence.

The disciplined practice of proactively injecting failures into a system in a controlled manner to test and improve resilience.

• Proactive vs. Reactive: While a postmortem is reactive (after an incident), chaos engineering is proactive, seeking to discover systemic weaknesses before they cause customer-facing issues.
• Synergy: Findings from chaos experiments often inform blameless postmortems and lead to the creation of new alerting rules and runbooks.

The practice of writing log messages in a consistent, machine-parsable format (e.g., JSON) with explicit key-value pairs.

• Critical for Analysis: Enables efficient filtering, aggregation, and correlation of events across all agents during an incident investigation.
• Postmortem Requirement: High-quality, structured logs from each agent are essential raw data for reconstructing events and performing root cause analysis without manual log parsing.

The calculated amount of acceptable unreliability for a service, defined as 1 - SLO. It quantifies how much "room for error" a team has for a given period.

• Governance Tool: Guides decision-making between velocity and stability. Spending the budget on incidents necessitates a focus on reliability work.
• Postmortem Context: The rate of error budget consumption helps prioritize which incidents warrant a deep-dive postmortem and subsequent investment in preventive measures.

A visual or data representation mapping the sequence of interactions, dependencies, and message flows between agents during a specific task execution.

• System Topology: Shows the runtime dependencies between agents, which may differ from the static architectural view.
• Postmortem Utility: Serves as a direct map for investigators, highlighting the propagation path of a failure through the agent network and identifying all affected components.