Use both. Here's where each one wins.
Observability tools see failures. Pisama acts on them. They are precondition layers; we are the action layer above. The comparison below is honest about where each one is stronger, not a zero-sum claim.
Pisama vs Operama
Operama is a Cornell-affiliated stealth startup that went public via the CAIS 2026 demo with the tagline Control Plane for Reliable AI Agents. The team is Vishwanath Katharki and Cornell faculty Sainyam Galhotra (also a co-author on the separate CAIS paper Trace-Level Analysis of Information Contamination in Multi-Agent Systems).
The pitch is goal decomposition into verifiable sub-goals, runtime monitoring, and automatic policy updates without retraining. As of the conference, there is no published benchmark, no public per-metric calibration, no pricing, and no integrations matrix. The product is at demo.getoperama.com.
Pisama is the alternative for teams that want a detector layer they can audit today. 34 production detectors, F1 published per detector, 59.9% on the TRAIL benchmark, and a reproducible calibration set.
- Strong narrative framing: control plane as a category-creating metaphor
- Cornell research credibility via Galhotra and the Prism Lab
- Automatic policy updates without retraining: a real product surface Pisama does not ship
- Shipping product with paying users vs pre-launch demo
- 34 production detectors with per-detector F1 published
- TRAIL benchmark 59.9% joint accuracy, against a published competitor benchmark
- MAST-aligned taxonomy with 7,212-trace calibration dataset, reproducible
- AgentPex implementation in production (specification_compliance F1 0.966)
- Framework adapters for LangGraph, CrewAI, AutoGen, OpenAI Agents, Claude Agent SDK, OpenClaw, n8n
At a glance
| Dimension | Operama | Pisama |
|---|---|---|
| Status | Pre-launch demo (CAIS 2026) | Production, paying customers |
| Public calibration | None published | F1 per detector, dataset open |
| Public benchmark score | None published | 59.9% on TRAIL (heuristic detectors) |
| Detector count | Not disclosed (sub-goal decomposition) | 34 production process-level detectors |
| Open source | Closed | MIT detectors, calibration data published |
| Distribution | Demo + Cornell network | API + 7 framework adapters + OTel ingest |
Recommendation
Operama is one to watch, especially if the Cornell research output continues to ship novel runtime techniques. For production today, Pisama is the calibrated detector layer with numbers you can verify. Once Operama publishes benchmarks and pricing, the comparison gets tighter.
FAQ
- Is the Operama and Pisama framing really different?
- The framings overlap. Both products operate at the runtime layer for multi-agent reliability. The difference today is that Pisama publishes per-detector F1 and a benchmark score; Operama publishes a demo. The frame becomes more accurate when both teams publish comparable numbers.
- Does Pisama do automatic policy updates without retraining?
- Not in the same sense. Pisama detectors generate fix suggestions and, for n8n today and other frameworks on the roadmap, automated patches via the self-healing pipeline. We do not modify agent policies in place; we surface detections that drive downstream changes.
- How does the Cornell research connection compare?
- Pisama implements published research too: the specification_compliance detector (F1 0.966) implements the AgentPex pattern from the Microsoft Research and University of Washington paper Willful Disobedience, also presented at CAIS 2026. Research-grounded is the baseline, not the differentiator.