# Traces connect cost, latency and results Canonical: https://semiaifoundry.com/learn/atlas/telemetry-traces-evaluation/ Revision: 2026-10-07 Rights: https://semiaifoundry.com/ai-atlas/licenses.html From request to durable action · Atlas v1.3.0 # Traces connect cost, latency and results Parent/child span identity, events, retries, token/byte counts and independent result evaluation. [Explore in Atlas](https://semiaifoundry.com/ai-atlas/#telemetry-traces-evaluation)Logical state, task evidence and declared units; no physical assembly scale Learning objectiveReconcile a trace’s elapsed time, service work and task result without double-counting concurrent intervals. ## The mechanism A trace connects observations through identities and time: request, attempt, tool call, service span, returned result and resulting artifact. It explains what the system did, but it does not by itself decide whether the task was correct. An HTTP success or completed span can carry the wrong entity’s answer. The independent evaluation should therefore join to the trace rather than reuse its success flag as a label. Time accounting also needs a declared boundary. Concurrent spans overlap, so their sum is total observed service work rather than elapsed latency. The incident experience has one worker and serial service, making the ledger hand-checkable. A separate worked parallel example demonstrates why summing child durations overstates wall time. Payload counters must say whether they include retries, serialization, headers or only logical arguments. 1. Stable IDs link the task to each proposal, attempt, service interval and external receipt. 2. Recorded start and end points distinguish waiting from service and retain failures rather than dropping them from metrics. 3. Elapsed time follows dependency paths; concurrent service durations are not simply added. 4. Independent task, source-support and policy checks attach to the same case identity after execution. ## Science and assumptions ### Traces connect cost, latency and results · explicit relationship $$ T_{\mathrm{wall}}=\max_{p\in\mathcal P}\sum_{i\in p}t_i $$ For a fixed dependency graph with unlimited resources, elapsed service time follows the longest dependency path. Symbols and units Twall Ideal elapsed service time for the graph ms 𝒫 Dependency paths from entry to completion path set tᵢ Duration of node i under the stated fixed schedule ms The normal incident uses services of 40, 60, 80 and 50 ms, totaling 230 ms. Four 5 ms proposals plus eight 1 ms schema/authority checks contribute 28 ms. Add a 50 ms queue delay and an assumed 200 ms review: elapsed time is 508 ms. These are synthetic inputs. In a different two-branch graph, parallel tasks of 40 and 60 ms followed by a 20 ms join take 80 ms, while summed service work is 120 ms. Neither trace establishes that the final answer is correct; that requires the separate environment check. Assumptions This ideal DAG bound excludes resource contention, arrival queues and human waits unless they are explicit nodes. The incident ledger includes those separate intervals and is serial. ## Engineering example The normal incident uses services of 40, 60, 80 and 50 ms, totaling 230 ms. Four 5 ms proposals plus eight 1 ms schema/authority checks contribute 28 ms. Add a 50 ms queue delay and an assumed 200 ms review: elapsed time is 508 ms. These are synthetic inputs. In a different two-branch graph, parallel tasks of 40 and 60 ms followed by a 20 ms join take 80 ms, while summed service work is 120 ms. Neither trace establishes that the final answer is correct; that requires the separate environment check. ## Distinctions and tradeoffs A successful span is not a successful user task, and summed parallel durations are not wall-clock latency. Detailed traces aid diagnosis but can add volume and expose sensitive data unless collection and redaction are deliberate. Agent and serving failures are located by connecting latency, calls and outputs to a common task identity. ## Specifications and interfaces Mechanism boundary Where did the system spend time and lose correctness? This ideal DAG bound excludes resource contention, arrival queues and human waits unless they are explicit nodes. The incident ledger includes those separate intervals and is serial. Evidence type Authored worked example The durable incident experience executes typed fixture tools, authority checks, exact review, receipts and independent final-state checks. Its policies are authored code and its storage boundary is local; it is not a real model or production workflow platform. ### Interfaces Declared input and state Input Stable IDs link the task to each proposal, attempt, service interval and external receipt. Checked outcome Output Reconcile a trace’s elapsed time, service work and task result without double-counting concurrent intervals. ## Check your understanding ### Two independent services take 40 and 60 ms, then a dependent join takes 20 ms. What is the ideal elapsed time? 1. 120 ms 2. 80 ms 3. 20 ms Answer and reasoning 80 ms The longer parallel branch finishes after 60 ms; the join then adds 20 ms. ## Evidence and scope The durable incident experience executes typed fixture tools, authority checks, exact review, receipts and independent final-state checks. Its policies are authored code and its storage boundary is local; it is not a real model or production workflow platform. The quantities and fixtures are authored. Source scope is recorded in the automation learning review manifest; no source benchmark or private system behavior is claimed. This ideal DAG bound excludes resource contention, arrival queues and human waits unless they are explicit nodes. The incident ledger includes those separate intervals and is serial. The durable incident experience executes typed fixture tools, authority checks, exact review, receipts and independent final-state checks. Its policies are authored code and its storage boundary is local; it is not a real model or production workflow platform. ## Sources - [OpenTelemetry GenAI semantic conventions](https://github.com/open-telemetry/semantic-conventions-genai) - [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://arxiv.org/abs/2406.12045) [Published lesson data](https://semiaifoundry.com/ai-atlas/lesson-data/telemetry-traces-evaluation-4f4ae5f22f915226.json) · [Atlas releases and licensing](https://semiaifoundry.com/ai-atlas/licenses.html) ## Continue exploring ### Foundations - [Evaluation & the AGI frontier](https://semiaifoundry.com/learn/atlas/evaluation/) - [Workflow graphs and explicit state](https://semiaifoundry.com/learn/atlas/workflow-graphs-state/) ### Related lessons - [Tools, memory & agent loop](https://semiaifoundry.com/learn/atlas/tool-loop/)