Here’s an uncomfortable truth about enterprise AI agents: most organizations deploying them have no idea if they’re working correctly.
They know the agent is running. They know it’s generating outputs. But are those outputs right? Are the agent’s intermediate steps logical? Is it spending credits responsibly?
Snowflake just shipped the tooling to answer those questions.
Cortex Agent Evaluations — GA March 13, 2026
Snowflake’s Cortex Agent evaluations reached general availability, providing three categories of metrics for monitoring agent behavior and performance:
1. Answer Correctness
How closely does the agent’s response match an expected answer? This is ground-truth evaluation — you provide the correct answer, the system measures how well the agent does.
Best for: static datasets where you know what “right” looks like. Think data analysis queries, report generation, or lookup tasks where there’s a definitive answer.
2. Logical Consistency
Does the agent’s planning make sense? Are its instructions, tool calls, and reasoning steps internally coherent?
This is reference-free — no dataset preparation required. The system evaluates whether the agent’s chain of thought holds together, regardless of whether the final answer is correct.
Best for: complex multi-step workflows where the process matters as much as the outcome. An agent might reach the right answer through flawed reasoning, which means it’ll fail on the next similar task.
3. Custom Metrics
Define your own evaluation criteria using prompts and scoring systems. Snowflake exposes the LLM judging process so you can build domain-specific checks — compliance validation, tone analysis, regulatory adherence, or whatever your use case requires.
Best for: industry-specific requirements that generic metrics can’t capture. Healthcare agents need HIPAA compliance checks. Financial agents need regulatory adherence scoring. Custom metrics make this possible without building evaluation infrastructure from scratch.
Full Activity Tracing
During evaluation, the agent’s activity is traced and monitored end-to-end. Every step — every tool call, every reasoning chain, every intermediate output — is recorded so you can verify that each action advances toward the intended goal.
This isn’t just logging. It’s causal tracing — understanding why an agent made each decision and whether the sequence of decisions was rational.
Resource Budgets (GA March 11)
Two days before evaluations shipped, Snowflake also launched resource budgets for Cortex Agents — controls for monthly credit spending with automated actions like access revocation when limits are hit.
This directly addresses the observability cost explosion we covered earlier this month. When enterprises are spending $80-150K/month on agent monitoring alone, capping credit consumption isn’t a nice-to-have — it’s survival.
Why This Matters Now
The agent observability gap is one of the biggest unresolved problems in enterprise AI:
- Gartner predicts 60% of AI agent deployments will fail, many due to inability to measure and correct agent behavior
- Monitoring costs have increased 4-8x as organizations deploy agents that make autonomous decisions
- DryRun Security found 87% of AI-agent PRs had security bugs — but most organizations couldn’t detect them before merge
- OWASP’s Agentic Top 10 lists inadequate monitoring as a core risk
Snowflake’s evaluations don’t solve all of this, but they establish a pattern: agent evaluation should be a platform feature, not a DIY project.
The OpenClaw Connection
OpenClaw approaches agent observability differently — through audit trails, command approval logs, and session history rather than statistical evaluation metrics. But the underlying need is identical: knowing what your agent did and whether it did it well.
For OpenClaw users running Claude or other models through Snowflake-connected data pipelines, Cortex Agent evaluations add a layer of monitoring at the data layer. Your OpenClaw agent orchestrates the work; Snowflake evaluates whether the data-touching parts of that work are correct.
The broader lesson: agent platforms that don’t ship evaluation tooling are asking users to trust blindly. Snowflake, AvePoint (AgentPulse), and the OWASP Agentic guidelines are all converging on the same conclusion — you can’t deploy agents you can’t measure.
What’s Still Missing
Cortex Agent evaluations are a strong start, but the harder problems remain:
- Cross-agent evaluation. What happens when multiple agents collaborate? Evaluating individual agents doesn’t capture emergent failures in multi-agent systems.
- Real-time intervention. Evaluation after the fact is useful for improvement. Real-time evaluation that can stop an agent mid-action is what enterprises actually need for high-stakes workflows.
- Standardized benchmarks. Every platform defines its own evaluation metrics. Without industry standards, comparing agent performance across platforms is impossible.
These are the problems that 2026’s second half will need to solve. For now, having any structured evaluation for AI agents is a significant step forward.
Cortex Agent evaluations are available in GA for all Snowflake accounts. See the Snowflake documentation for setup details.