Agent Reliability Index
Compare agent reliability without hiding the conditions behind the score
Agent Reliability Index becomes relevant when several candidate agents appear strong on demonstrations but have been tested on different work. A focused review would examine a completed task under declared tools, permissions, interruption conditions and review rules. This is most relevant to product leaders and evaluation researchers, especially where teams must distinguish first-attempt success and recovered completion.
Connect the operating question to inspectable evidence
- Product leaders and evaluation researchers
- Owns the bounded decision and the consequence of acting.
- Versioned task samples
- Defines the starting context for the proposed work.
- First-attempt success versus recovered completion
- Exposes the first material uncertainty to examine.
- A shortlist decision brief with unresolved comparisons
- Carries unresolved conditions into an accountable next action.
Decision at stake
Compare agent reliability without hiding the conditions behind the score
Make the decision explicit
- Product leaders and evaluation researchers
- Several candidate agents appear strong on demonstrations but have been tested on different work.
- Decision
- Which agents merit a deployment-specific trial and which reliability gaps need further investigation.
- First-attempt success
- Use versioned task samples to establish the relevant starting condition.
For product leaders and evaluation researchers, the immediate question is which agents merit a deployment-specific trial and which reliability gaps need further investigation. This is most useful when there is a real choice between proceeding, changing the design, collecting more evidence or narrowing the intended use. The starting conversation should identify that choice and its consequence before deciding how much work the topic warrants.
An overall score could conceal a weak performance stratum, especially if the easiest tasks dominate the sample. A useful review follows a completed task under declared tools, permissions, interruption conditions and review rules, rather than stopping at a convenient component metric or document boundary. The evidence should separate observation, interpretation and recommendation so the accountable owner can see what it supports.
Coverage
First-attempt success and recovered completion need different evidence
Keep the review distinctions visible
- First-attempt success
- Compare with recovered completion.
- Task difficulty
- Compare with aggregate rank.
- Model response quality
- Compare with completed business work.
First-attempt success versus recovered completion
We would define separate outcomes for unaided completion, successful recovery and completion dependent on a person. Any decision about unattended operation needs that distinction even when every final artefact looks acceptable.
Task difficulty versus aggregate rank
The task population would be stratified around meaningful difficulty and consequence. Changing the share of easy cases should not silently improve the reported reliability; sensitivity analysis would show where the comparison depends on weighting.
Model response quality versus completed business work
Resource conditions would include tool availability, execution limits and reviewer assistance. A result obtained with privileged access or repeated manual rescue would remain useful evidence, but its interpretation would identify those conditions.
Prerequisites
Start with versioned task samples
Give each input a specific job
- Versioned task samples
- Defines the operating boundary.
- Unsuccessful and abandoned runs
- Examine task difficulty.
- Execution budgets and human intervention logs
- Shows conditions affecting model response quality.
- Research sponsor
- Confirm access and a reviewer who can resolve gaps.
For Agent Reliability Index, a research question and permitted evidence route should be agreed before a dataset is assembled. Contributions would need provenance, inclusion rules and a record of collection conditions. Where a participating organisation supplies only summaries, the methodology should explain how that limits verification.
Useful starting material would include versioned task samples; unsuccessful and abandoned runs; execution budgets and human intervention logs. These inputs have different jobs: the first establishes context, the second supplies behaviour or comparison material, and the third exposes operating conditions that may qualify a conclusion. Existing material would be reviewed before requesting new collection, and a missing input would be recorded as an evidence gap rather than treated as a favourable result.
How the work proceeds
Protect the comparison before interpreting the research
Follow the evidence through the work
- Versioned task samples
Define the comparison and freeze the conditions it covers.
- Unsuccessful and abandoned runs
Collect this material under the declared inclusion and handling rules.
- Task difficulty
Challenge competing explanations before drawing a comparative conclusion.
- A shortlist decision brief with unresolved comparisons
Retain reproducibility gaps and the research question that remains open.
Frame the comparison
Define the task taxonomy and completion rubric before collecting results. The opening step turns the operating concern into a question that can be examined. It would identify a completed task under declared tools, permissions, interruption conditions and review rules, the outcome the owner cares about and the conditions that would make an observation decision-relevant. Scope changes would remain explicit if the evidence reveals a different problem.
Collect comparable evidence
Run comparable configurations with explicit resource and intervention limits. The comparison material would include unsuccessful and abandoned runs, with execution budgets and human intervention logs available to explain the operating conditions. Differences between first-attempt success and recovered completion would remain visible beside the observation rather than disappear into a pooled result.
Challenge interpretation
Inspect disagreement, failed attempts and sensitivity to the task mix. The review question would explicitly distinguish task difficulty from aggregate rank. A material discrepancy would be traced back to versioned task samples and the relevant execution or decision record, so a proposed change addresses the observed cause rather than the most convenient explanation.
Bound the conclusion
Report disaggregated findings with reproducibility and appropriate uncertainty. The resulting shortlist decision brief with unresolved comparisons would explain the distinction between model response quality and completed business work and identify what further observation would change the recommendation. The research sponsor would retain the decision, including whether a narrower scope or a different approach is preferable.
Illustrative scenario
Supplier record mix: examine what changes the decision
Examine the constructed decision boundary
Illustrative scenario
- Supplier record mix
One candidate handles routine records well; another resolves conflicting details.
- Stratified comparison
Keep easy and ambiguous tasks separate when interpreting completion.
- Local shortlist
Select a deployment-specific trial without declaring a universal winner.
Imagine an internal research team comparing agents that prepare supplier records. One agent completes more simple records, while another handles conflicting supplier details more cautiously. A proposed index would retain both results, show the task mix and record how reviewers resolved ambiguity. The example would support a shortlist for local testing, without declaring either system universally more reliable.
The comparison could change for a workload dominated by disputed records rather than routine updates. The proposed publication would therefore expose those strata and the intervention burden. A shortlist decision would remain conditional on the organisation’s workload, with a targeted local trial as the next evidence step. These are hypothetical decision paths, not observed client outcomes.
Indicative artefacts
Use a shortlist decision brief with unresolved comparisons to make the next action explicit
Every artefact needs an operating use
Illustrative working format
- An index methodology and inclusion policy
- Boundary — versioned task samples.
- A versioned reliability evidence table
- Findings about task difficulty.
- A shortlist decision brief with unresolved comparisons
- Action on model response quality.
The final artefact set depends on the agreed decision. Useful outputs can include:
- An index methodology and inclusion policy. The working foundation for the question, including relevant definitions, boundaries and assumptions so another reviewer can understand what was examined.
- A versioned reliability evidence table. The substantive observations and interpretation, preserving adverse results, disagreement and limitations that affect how the evidence may be used.
- A shortlist decision brief with unresolved comparisons. The connection to action: what the owner could do next, what evidence is still missing and what change would justify revisiting the conclusion.
Scope and authority
A research proposal does not establish an index or assurance scheme
Keep analytical work separate from decision authority
- Declared research question and protocol
- Can include an index methodology and inclusion policy.
- Research sponsor
- Research scope, contributors and publication rights require explicit agreement.
- Universal rankings or accredited assurance
- Cannot be inferred from a versioned reliability evidence table.
A proposed research asset is not an established index, certification scheme or delivered client result. Publication, contributor participation and any public comparison would require separate authorisation. Reviewer competence, conflicts and the permitted conclusion would be confirmed for the actual assignment. To discuss agent reliability index, the first practical step is to identify the relevant owner and bring a sanitised description of the decision, the current alternative and the material uncertainty. That gives scoping a concrete starting point without implying a preapproved package or favourable conclusion.