How do I add compliance logging to AutoGen agents?

The simplest approach: prompt your final decision agent to end its recommendation with structured JSON (decision, confidence, reasoning fields). After each conversation completes, parse the structured output from the last message and call ghost.capture() with subject_id (who was affected), decision_type, context (conversation inputs), reasoning, and action. Ghost SDK fires asynchronously — under 5ms overhead, no blocking of your agent workflow. For GroupChat workflows, add a dedicated ComplianceRecorder agent that outputs a COMPLIANCE_RECORD at conversation end.

What is the difference between AutoGen chat logs and compliance records?

AutoGen chat logs capture everything that happened in the conversation: all messages, token counts, tool calls. Compliance records are structured, per-decision entries linking the affected person, the reasoning, and the outcome. Chat logs are useful for debugging; compliance records satisfy EU AI Act Article 12 (post-hoc reconstruction), HIPAA §164.312(b) (audit controls), GDPR Article 22 (meaningful information about automated decision logic), and SOC 2 CC7.2 (anomaly detection). Chat logs are typically mutable and stored in AutoGen's in-memory structures; compliance records must be tamper-evident and retained under organizational control.

How do I handle HIPAA requirements in AutoGen clinical agents?

For HIPAA compliance in AutoGen: never include raw PHI in the ghost.capture() context field — use an internal patient reference ID and store PHI-containing records in an encrypted, separately access-controlled store. The audit log must capture: the patient identifier, the accessing agent (by name/version), the timestamp, and what type of clinical data was accessed. Retain audit records for at least 6 years. Access to audit records must itself be logged. Use Ghost SDK's VPC deployment option if your security requirements prohibit storing audit records in a shared cloud.

How do I ensure deterministic replay for AutoGen agents?

AutoGen's conversational model is inherently non-deterministic — re-running the same inputs with default settings may produce different conversations. For drift detection and EU AI Act post-market monitoring, use temperature=0 in your LLM config for evaluation runs, set the OpenAI API seed parameter for reproducibility, and use Ghost SDK's semantic similarity comparison rather than exact text matching when comparing current decisions against historical baselines. The threshold-based alerting fires when semantic similarity drops below your configured value, indicating behavioral change from a model provider update.

How to Add Compliance Audit Logging to AutoGen Multi-Agent Systems

AutoGen's conversational multi-agent model makes compliance logging more complex than single-step frameworks — the decision is the conclusion of a multi-turn conversation, not the output of a single function call. This guide explains three patterns: post-conversation decision capture (extract structured JSON from final agent message), ComplianceRecorder agent in GroupChat (dedicated agent extracts compliance record), and nested agent correlation IDs for multi-pipeline decisions. All patterns use Ghost SDK for fire-and-forget capture under 5ms.

The AutoGen Compliance Challenge

AutoGen provides complete conversation histories — role, content, timestamps for all messages, tool calls and returns, token costs. What compliance frameworks require but AutoGen does not structure: subject_id (who was this decision about?), decision_type (what kind of decision?), final action (the structured outcome, not just the last message text), confidence, tamper-evident record with cryptographic seal, and organization-controlled storage. The solution is extracting a structured decision record from the conversation and capturing it with Ghost SDK after each conversation.

Pattern 1: Post-Conversation Capture

The simplest approach: prompt the final decision agent to end with structured JSON containing decision, confidence, and reasoning fields. After the conversation completes, parse the JSON from the last message and call ghost.capture() with subject_id, decision_type, context (complete input to the conversation), reasoning, action, confidence, and metadata including model version. This pattern requires zero changes to existing agent logic — just add the post-conversation capture step.

Pattern 2: ComplianceRecorder in GroupChat

For complex GroupChat workflows, add a dedicated ComplianceRecorder agent. This agent monitors the conversation without participating in the substantive discussion. When the GroupChatManager signals the conversation is approaching its end (round limit or termination condition), the ComplianceRecorder outputs a COMPLIANCE_RECORD JSON. After the conversation, extract this record from the chat history and send it to Ghost SDK. The ComplianceRecorder pattern provides cleaner separation between decision logic and compliance capture.

Nested Agent Pipelines with Correlation IDs

When AutoGen conversations call other agent conversations as sub-tasks, use a UUID decision_id to link all sub-conversations to the parent decision. Generate the ID at the pipeline entry point, pass it to each nested conversation via context or config, and include it in every ghost.capture() call. When a regulator or auditor requests the complete decision trail for a specific case, querying by decision_id returns the full pipeline from orchestrator intent through each sub-conversation to the final outcome.