Generative AI Observability
Complete visibility into AI agent operations. Track git commits, shell commands, file changes, and API calls in real-time. SOC 2 compliant audit trails for compliance and security. For broader AI risk management guidance, see the NIST AI Risk Management Framework.
What GAL observes in AI agent sessions
AI agents operate at machine speed. GAL captures every operation so you can understand what happened, when, and why.
Git Operations
Every git commit, push, pull, branch, and merge. See exactly what code changes agents made, when, and in what context.
git commitgit push origin maingit checkout -b featureShell Commands
All terminal commands executed by agents. Know exactly what scripts ran, what packages were installed, what files were modified.
npm installdocker buildrm -rf distFile Modifications
Read, write, and edit operations on your codebase. Track which files agents accessed and changed.
Read: src/auth.tsWrite: .envEdit: package.jsonAPI Calls
External network requests made by agents. Monitor which services agents communicate with and what data flows out.
POST api.stripe.comGET github.com/reposPUT s3.amazonaws.comReal-time operation feed
Watch AI agent operations as they happen. Filter by type, search by keyword, and drill into any operation for full context.
git commit -m "Add auth middleware"claude-code✓successWrite: src/middleware/auth.tsclaude-code✓successnpm run lintclaude-code✓successGET api.github.com/user/reposclaude-code✓successgit push --force origin mainclaude-code✕blockedWhy generative AI observability matters
Without visibility, AI agents are a black box. GAL illuminates every operation so you can govern with confidence.
Security Investigation
When something goes wrong, trace exactly what the agent did. See the full command history, file accesses, and external calls that led to the incident.
Compliance Audits
Generate audit reports showing every operation during a given period. Demonstrate to SOC 2, ISO 27001, or HIPAA auditors that AI agents operated within approved boundaries. Aligned with the NIST AI Risk Management Framework for responsible AI governance.
Debugging Agent Behavior
Agents sometimes make unexpected decisions. The operation log shows exactly what commands they ran and what files they accessed, helping you understand why.
Performance Monitoring
Track how many operations agents perform per session. Identify patterns, optimize workflows, and measure the impact of policy changes.
Key benefits of AI agent observability
Real-Time Visibility
Watch operations as they happen, not after the fact. Respond to issues immediately instead of discovering them in post-mortems.
Searchable History
Every operation is logged and searchable. Find specific commands, filter by time range, or search across all your AI agent sessions.
Anomaly Detection
Flag unusual patterns automatically. Get alerted when agents perform operations outside normal behavior for your organization.
GAL vs. traditional monitoring tools
| Capability | Traditional Tools | GAL |
|---|---|---|
| Agent-specific context | No | Yes |
| Operation-level granularity | Process-level | Command-level |
| Intent classification | No | Yes |
| Multi-agent support | Varies | 6 platforms |
| Compliance-ready export | Manual | One-click |
How AI agent observability works
GAL intercepts operations at the runtime level, capturing everything before it executes.
Intercept
GAL wraps your AI agent\'s execution environment. Every operation passes through the GAL layer before executing.
Log
Operations are logged with full context: timestamp, agent, operation type, parameters, and result. All stored securely in your GAL workspace.
Analyze
View operations in real-time or search historical data. Export for compliance, investigate incidents, or optimize agent workflows.
Frequently asked questions
What is generative AI observability?
Generative AI observability is the ability to see and understand what AI agents are doing in real-time. It goes beyond traditional monitoring by capturing agent-specific operations like file reads, shell commands, git operations, and API calls with full context.
Why do I need observability for AI coding agents?
AI agents operate autonomously at machine speed. Without observability, you have no visibility into what they did, when, or why. This creates security blind spots, makes debugging difficult, and prevents meaningful governance.
Does observability slow down my agents?
No. GAL adds less than 5ms of latency to each operation. The overhead is negligible for interactive use and imperceptible for automated workflows.
How long is operation history retained?
Retention depends on your tier. Free tier keeps 7 days of history. Paid tiers offer 90 days to unlimited retention for compliance requirements.
Can I export operation logs for compliance audits?
Yes. GAL supports one-click export of operation logs in CSV and JSON formats. Generate reports filtered by date range, agent, operation type, or user for SOC 2, ISO 27001, and HIPAA audits.
See what your AI agents are doing
Get complete visibility into AI agent operations in under 5 minutes. Free tier available.