agentloss 0.0.11

Measure the real-world error rate and dollar cost of an AI agent's decisions. OpenTelemetry-native.

agentloss

Your eval tool tells you your AI agent's hallucination rate. agentloss tells you what it costs. An OpenTelemetry-native SDK that measures the real-world error rate and dollar loss of an AI agent's decisions — by capturing its consequential actions in-process and joining them to ground truth (real resolved outcomes, not an offline labeled set).

Every eval/observability tool scores quality proxies — LLM-judge, hallucination rate, task completion. agentloss answers the question the market keeps asking and no tool measures: what are my agent's mistakes costing, and is it safe to trust with more autonomy?

Part of ADMT (Automated Decision-Making Technology) — admt.ai.

Install

pip install agentloss

Quickstart

Instrument only the consequential action — the tool call that moves money or commits the business — not every LLM call.

from agentloss import decision, report_outcome, Decision

@decision                                     # bare decorator; the returned Decision is recorded
def approve_payment(invoice):
    action = run_matching(invoice)            # "approve" | "hold" | "reject"
    return Decision(action=action, value_at_risk_usd=invoice.total,
                    business_key=invoice.number, use_case="ap_3way_match")

# when the outcome resolves (correction, dispute, chargeback, audit, human review):
report_outcome(business_key="INV-1", ground_truth="duplicate-should-block",
               source="recovery_audit", realized_loss_usd=14200)

You already have the ground truth? (the common case — a disputes / chargebacks table). That's the default: each reported outcome is a census observation that counts toward the number, no flags needed. Join the whole table in one line:

from agentloss import record_outcomes

record_outcomes([
    {"business_key": "INV-1", "ground_truth": "reject", "source": "chargeback",
     "realized_loss_usd": 80.0},
    {"business_key": "INV-2", "ground_truth": "approve", "source": "dispute"},  # a CORRECT one
])

Report the outcomes that agreed with the agent too, not only the disputes — the rate's denominator is reported approvals, so reporting only errors makes it read ~100%. source is one of recovery_audit | dispute | chargeback | refund | human_queue | verification_agent.

It computes the error rate by segment (with confidence intervals), realized + expected dollar loss, and the agent's incremental risk vs. a baseline. Raw prompts/records stay in your boundary; only derived metrics leave.

Confirm the wiring — agentloss.doctor() inspects the store and catches the silent failures in plain language (outcomes reported but none counted, only-errors reported, a loss source that won't be summed). Or from a shell: agentloss doctor --json.

Works with your existing traces (Phoenix / Langfuse / Braintrust / OTel)

Already tracing your agent with OpenInference/OpenTelemetry? Don't re-instrument. Add a few agentloss.* attributes to the consequential span, point agentloss at your spans, and it adds the loss/outcome layer on top of what your tracer already emits:

from agentloss import ingest_spans, sample_and_verify, print_report

ingest_spans(your_spans)       # OTel/OpenInference spans carrying agentloss.* attributes
sample_and_verify(verify_fn)   # Tier A: get a number with no external labels wired
print_report()                 # error rate by segment + dollar loss

See examples/from_spans.py.

How it works

• Instrument consequential actions, not the whole agent. The costly events are the handful of tool calls that move money or commit state.
• Ground truth arrives late, from outside the agent — a correction, dispute, audit result, or human review. Capture it via report_outcome, the human-review queue, and active sampling
  • a verification agent. This is real resolved outcomes, not an offline dataset.
• Honest statistics. Monetary-unit sampling with a target verifier budget; two-phase calibration corrects a fallible verifier's bias back to truth (with confidence intervals).

See docs/SDK-SPEC.md for the full API, agentloss.* semantic conventions, and the pack/adapter model.

Try the demo

An oracle-validated harness that seeds an accounts-payable environment with known errors and checks that agentloss recovers the true error rate and dollar loss:

python -m dogfood.run                                  # deterministic mock, no deps
AGENTLOSS_VERIFIER_LLM=claude ANTHROPIC_API_KEY=... python -m dogfood.run

For AI coding agents

agentloss is built to be discovered and wired by coding agents: llms.txt, the instrument-agent-reliability skill, the AGENTS.md rule, and an MCP server (how_to_instrument, explain_attribute, validate_integration).

License

Apache-2.0.