Everything you need to govern AI agents

Problem

Orchestrator calls Research Agent and Summarizer. Each works independently. Where's the bottleneck? How much does each step cost?

Solution

Aberon links all spans via parent_trace_id. One page shows the entire chain: orchestrator → research → summarizer, with cost, latency, and token count per span.

Problem

At 3 AM one of 5 agents starts returning errors. By morning, 200+ tickets have no response.

Solution

Real-time SSE dashboard. Quick filters for errors, slow traces, expensive runs. Drill down to the exact span and error message. Problems visible in seconds, not hours.

Problem

Support agent has access to search_kb, create_ticket, send_email, execute_sql. The last two are dangerous — the agent could hallucinate and send an email or run SQL against production.

Solution

One policy: block execute_sql and send_email. Agent cannot call them. Every attempt is logged in audit trail. Setup takes 2 minutes, no code changes.

Problem

Data Analysis Agent processes large datasets through GPT-4. Normal request costs $0.50. But sometimes the agent enters a loop and racks up hundreds of dollars.

Solution

Cost limit policy: if trace exceeds $50, agent pauses and requires human approval. Analyst sees full context, approves or rejects. Every decision recorded in audit trail.

Problem

A bank uses AI for credit decisions. The regulator asks: which decisions did AI make, who approved them, and prove the logs haven't been modified.

Solution

Every audit entry includes a SHA-256 hash of itself plus the previous entry's hash. Modify any record and all subsequent hashes break. One-click verification.