Cite as: Real Problem AI problem “Why can't I tell if my AI agent is actually doing what it said it did?”. Opportunity score 8.6 out of 10 (severity 9, AI feasibility 9, market signal 9, competition gap 7). Category Others. Trend LLM. Source signal: Google Cloud Data Analytics, 10 Jul 2026, on why pass/fail benchmarks do not map where agent capability breaks.. Canonical URL: https://www.realproblem.ai/archive/why-cant-i-tell-if-my-ai-agent-is-actually-doing-what-it-said-it-did.
Why can't I tell if my AI agent is actually doing what it said it did?
Teams ship agents that call tools, hit production data, and report back in natural language. Trace logs are JSON soup. There is no per-run verdict on whether the agent did the right thing, just token counts and latency.
Who has it: Eng leads at startups running production LLM agents on Anthropic, OpenAI, or open-weights inside Langfuse, Braintrust, Arize.
- Severity9/10
- Feasible today9/10
- Market signal9/10
- Competition gap7/10
Heard in public
“We are handing our agents a pass/fail exam when what we actually need is a map of the agent's capabilities.”
Quoted word for word from the public post linked below. Nobody submitted it to Real Problem AI.
Google Cloud Data Analytics, 10 Jul 2026, on why pass/fail benchmarks do not map where agent capability breaks.What nothing does yet
A drop-in agent scorer that reads each trace, infers the user goal, replays tool outputs against the team's policy, and emits a clear verdict with a short reason. Results should be grouped by tool, user segment, and agent version so regressions are visible.
Tools that solve this
4 named so far, and where each one stops.
- Langfuse
Strong tracing, weak per-run correctness verdict
- Braintrust
Great eval harness, runs offline, not on live traffic
- Arize Phoenix
Observability, no agent-task scorer out of the box
- LangSmith
Tied to LangChain, scoring is BYO
Why it is archived
Retired in the 2026-10-25 freshness audit: agent verification is covered by newer, sharper entries on the live list.
How you might build it
- 01OpenTelemetry trace ingest with tool-call schema awareness
- 02Goal-inference LLM pass over the first user turn
- 03Policy DSL for what counts as success per tool
- 04LLM-judge plus ground-truth replay on sampled traces
- 05Regression-detection dashboard per agent version
Founders working in this area
- Alex Reibman, Co-founder, AgentOps
- Nir Gazit, Founder and CEO, Traceloop
Not affiliated with Real Problem AI.
#O14 · Canonical URL: https://www.realproblem.ai/archive/why-cant-i-tell-if-my-ai-agent-is-actually-doing-what-it-said-it-did