One connected reliability workspace
Tensile connects the question, evidence, decision, change and outcome. Explore the proposed workspace below. Follow one case across six product areas, or switch to a different kind of agent work.
Tensile / Customer job R-104
Production finding
F-104 · duplicate saved records · one known affected job
Investigation
I-104 · question, skill v1, source cutoff and explicit owner choice
Evaluation
E-104 · exact-one-record expectation · four pinned Check versions
Illustrative records · proposed experience
Duplicate records after a tool timeout
One affected fictional job · source cutoff 21 Oct · coverage partial
Two records share request R-104. The agent retried after losing the write response.
The database committed before the response was lost. The retry did not reconcile the request identity.
Recurrence and wider customer impact remain unverified.
Inspect population membership and source coverage
One included affected job, one excluded job, one unknown job. No recurrence rate or broader accuracy is established.
Request · R-104 · source revision 3
request_id: R-104 revision: 3 requested_effect: one approved record confirmation_owner: owner-12
Authored specimen · source cutoff 21 Oct 2026 · fixed illustrative source cutoff · Investigation I-104/v1. This is an inspectable fictional record, not a customer trace.
Approval · A-12 · authorized owner
approval_id: A-12 request_id: R-104 actor: owner-12 authorized: true allowed_destination: requests
Authored specimen · source cutoff 21 Oct 2026 · fixed illustrative source cutoff · Investigation I-104/v1. This is an inspectable fictional record, not a customer trace.
Write journal · J-44 · committed, response lost
journal_id: J-44 operation: insert(request_id=R-104) commit: D-22 response: timeout AFTER commit retry: second insert → D-23
Authored specimen · source cutoff 21 Oct 2026 · fixed illustrative source cutoff · Investigation I-104/v1. This is an inspectable fictional record, not a customer trace.
Destination · D-22 and D-23 · same request
destination: requests query: request_id = R-104 records: [D-22, D-23] count: 2
Authored specimen · source cutoff 21 Oct 2026 · fixed illustrative source cutoff · Investigation I-104/v1. This is an inspectable fictional record, not a customer trace.
Clickable product concept · invented records · no external execution.
A closer look / illustrative case
The workspace follows R-104 through its entire reliability lifecycle. The records below are the concrete objects each team keeps, rather than six disconnected dashboards.
Production finding
F-104 · duplicate saved records · one known affected job
Investigation
I-104 · question, skill v1, source cutoff and explicit owner choice
Evaluation
E-104 · exact-one-record expectation · four pinned Check versions
Environment
R-104 · actor, approval, empty destination, timeout after commit
Improvement
C-104 · reconcile before retry · paired run evidence
Outcome
J-104 · required stages, saved result, harm rules and cutoff
The links preserve provenance as work changes hands. No claim of a live customer execution is made by this preview.
Production intelligence
Inspect recorded executions, recurring findings and feedback in their customer and release context. Keep the eligible population, exclusions, freshness and evidence gaps beside the signal.
— Business-language execution paths with underlying event evidence
— Recurring findings tied to distinct customer work
— Alerts that lead to the affected case and next action
Explore production intelligence ↗
Investigate
Start with a business question and intended behavior. Generate and refine a scoped skill over permitted sources. Examine findings, trajectories and evidence. Keep explicit human decisions separate from suggestions, and compare immutable reruns.
— Summary, Trajectory, Evidence and Versions
— Skill inputs, runs, decisions and learnings
— Confirm a finding into Evaluations with its origin intact
Explore Investigate ↗
Executions
Organize Evaluations into Packs and compose them from reusable, versioned Checks. Review examples, recorded results and independent validation separately. Run again on a defined schedule with visible execution receipts.
— Deterministic checks and model judgments for different evidence
— Exact versions and inputs in every run manifest
— Pass, Fail, Unknown, held and incomplete results stay distinct
Explore Evaluations and Checks ↗
Environments
Use controlled tool state and scoped execution to test complete workflows. Pin mailbox or workspace identity, records, permissions and clock. Inject a failure at the operation that matters. Verify setup, task, policy and evidence separately.
— Stateful tools, recorded replay and explicit unsupported behavior
— Reset proof and equivalent inputs for each comparison
— Independent state inspection and an action journal
Explore environments ↗
Improvements
Connect a source-backed failure and cause hypothesis to a prompt, code or configuration proposal. Compare the candidate on original failures, previous successes and known misses. Give the owner the tested revision and remaining limits.
— Matched baseline and candidate runs
— A reviewable change proposal or customer PR where supported
— Human approval, release and observed benefit remain separate
Explore tested improvements ↗
Outcomes and learning
Follow whole customer jobs rather than counting successful spans. Link later changes and recovery. Define the business measure with its sources and missing-data rules. Retain qualified lessons for later investigations and regression checks.
— A versioned outcome for each customer job
— Business measures with reproducible included and excluded records
— Tested workspace lessons with review and rollback
Explore outcomes ↗
Before the first workflow
The website demonstrates the proposed workflow with authored records and local interactions. It does not run a customer agent, call a provider, persist server records or establish production availability.
Scope one customer workflow, its permitted sources and the authoritative result. Review supported tool boundaries, access, reset fidelity and missing evidence before enabling execution.
The business expectation, source permissions, domain review and release decision have explicit owners. The product keeps the evidence and versions connected so those decisions are reviewable.
Start with an affected customer job, the expected result and the evidence your team would accept.
Discuss a concrete workflow ↗
AI reliability, from production to customer outcome.