Your monitoring has a blind spot
Traditional observability records infrastructure signals: latency, error rates, and token counts. It cannot see an agent's output get worse, and the eval scores layered on top stop at telling you a number moved. When your agent degrades in production, Tessary tells you which change caused it, in time to fix it before it does damage.
- APM (Datadog, Grafana)
- Latency, error rates, token counts, HTTP status codes. Infrastructure health, no view of output quality.
- LLM observability (Langfuse, Braintrust)
- Traces, prompts, and eval scores you assemble yourself. They tell you a score moved, not which change moved it.
- Agent reliability (Tessary)
- Reads the trend over graders, then traces a degradation to the causing change. Pulls from your existing Langfuse or Braintrust traces, no re-instrumentation.
What the published data shows
A NeurIPS benchmark study and Datadog's telemetry point at the same gap: the failures that matter most don't appear on an infrastructure dashboard.
- 41–86.7%
Task failure rates on benchmark tasks across seven popular multi-agent frameworks
MAST, NeurIPS 2025
- 60%
Of LLM call errors in February 2026 were caused by exceeded rate limits, the failure class most likely to be masked by retries
Datadog, State of AI Engineering 2026
Full observability. Invisible failures.
In April 2026, engineers running Claude Code with full observability tooling had no automated signal that behavior had degraded. Anthropic's postmortem records that infrastructure modifications altered agent behavior for weeks before user complaints surfaced the problem. Latency and error rates showed nothing unusual throughout.
Anthropic postmortem, Apr 2026 ↗Weeks
Agent behavior degraded before any monitoring alert fired
Zero
Infrastructure alerts during the full degradation period
What each layer answers
Datadog and Grafana sit a tier below both columns: infrastructure health, with no view of output at all. LLM observability holds the traces and tracks the scores. Agent reliability starts when a score moves and ends at the change that moved it. The layers are complementary, not competing.
LLM observability
Langfuse, Braintrust, and eval platforms
- Traces, prompts, and outputs stored and searchable
- Eval scores tracked over time
- Latency, token counts, and cost per call
- Degradation read as a trend across graders
- A score drop traced to the change that caused it
- Causes with no diff: provider updates, upstream agent shifts
Agent reliability
Tessary, on top of your existing traces
- Traces, prompts, and outputs stored and searchable
- Eval scores tracked over time
- Latency, token counts, and cost per call
- Degradation read as a trend across graders
- A score drop traced to the change that caused it
- Causes with no diff: provider updates, upstream agent shifts
Failures your stack won't flag
These failure classes produce no HTTP error, no exception trace, and no latency spike. They are invisible to APM tools by design.
Hallucinated tool calls
The model invokes a nonexistent or incorrect function. The request returns HTTP 200 with no error trace. Infrastructure metrics look healthy while the agent output is wrong.
Output quality decay
After a model version update, accuracy drops gradually. No threshold alert fires because no infrastructure metric changed. The degradation is invisible until users report it.
Rate-limit retry masking
A retry succeeds with a fallback model but delivers a degraded response. The span records a successful retry, not the quality loss. No metric captures the difference.
Multi-agent coordination gaps
A sub-agent silently skips a step. The orchestrator proceeds with incomplete context. No exception fires. The failure is visible only in the final output.
Four stages of reliability
Tessary layers on top of the traces you already have. The flow ends at the cause, not at an alert that a number moved.
Connect your trace source
Link Tessary to Langfuse or Braintrust as an upstream source, or send traces through the OTLP receiver. Tessary pulls a chosen slice of your already-instrumented traces on demand. No instrumentation changes required to start.
Detect degradation as a trend, not a threshold
Tessary scores traces against graders and reads the trend across verdicts over time, not any single score. An imperfect grader misfires at a stable rate, so a failure rate that jumps from 3% to 11% signals a real change in the agent. Detection holds up before your graders are perfect.
Trace the drop to the change that caused it
A failing grader names the quality dimension that broke. Tessary walks that dimension back through the agent's chain to the change responsible: a prompt edit, a tool update, a dependency bump, an upstream agent's output shift, or a model provider update that never touched your repo. When the trail hits a gap in the traces, that gap becomes the next node to instrument, and the next hunt is faster.
Route the finding, cause attached
The alert lands in Slack, Sentry, PagerDuty, Linear, or a webhook, and it names the cause rather than just the score that moved. The same attribution engine can also run before a merge and post a risk report on the PR, for the causes that do ship through one.
Answered, plainly.
Related pages
Tessary vs Langfuse and Braintrust
The comparison in detail: where trace stores stop and cause-finding begins.
Agent observability setup
Get traces flowing first: OTLP, the SDKs, or a pull from your existing store.
Agent reliability for CTOs
The case for a reliability layer when your agents do work that matters.
Find what caused it before users report it
Connect your Langfuse or Braintrust traces in one configuration step, with no instrumentation changes and no credit card, and get from a score drop to the change behind it.