AI agents often look impressive when they answer a clean question using one tidy data source. Enterprise work is harder. Important signals are distributed across engineering systems, customer records, support conversations, and internal documentation. The agent must decide what matters, connect evidence across those systems, and recommend a responsible response without inventing facts or claiming actions it did not perform.
That is the problem I explored in my contribution to Enterprise-Bench, an open benchmark for evaluating enterprise AI agents. My pull request adds a proposed Level 3, cross-domain task focused on payment incident triage: devrev/enterprise-bench#50.
Disclosure: DevRev offered me $25 to publish an article about Enterprise-Bench and my contribution. Positive coverage was not required. The views and lessons in this article are my own.
What Enterprise-Bench evaluates
Enterprise-Bench is designed around the conditions an agent encounters inside a real organization. The benchmark evaluates whether an agent can retrieve the right evidence, reason across business systems, remain within access boundaries, and produce a useful, auditable result.
The published L1–L2 suite covers reactive retrieval and analytical, multi-source questions. My contribution explores an L3-style task, where the agent is given an objective but not a prescribed investigation path. Instead of following an explicit list of records to retrieve, it must determine which systems and evidence are relevant, distinguish the most consequential incident from background noise, and coordinate a proposed response.
The task I contributed
The scenario asks an agent to review a fictional company's current operational situation involving money movement, settlement, and financial-state integrity. The agent must investigate the available engineering, customer, and internal business evidence and then:
- identify the most consequential problem;
- explain which customers and business outcomes are affected;
- assess operational or compliance exposure without overstating the evidence; and
- recommend a coordinated cross-functional response.
The task uses seeded, synthetic benchmark data and read-only systems. Its output is an evidence-grounded incident assessment and response recommendation—not a claim that the agent changed tickets, released payments, issued refunds, or performed remediation.
Why this is an L3 task
The key design choice is that the prompt does not tell the agent which incident, customer, engineering issue, or internal control to inspect. A successful agent has to form its own investigation plan.
The task tests whether the agent can:
- Select relevant evidence sources across engineering, CRM/customer, and internal business data.
- Correlate customer-facing symptoms with active engineering problems.
- Separate a systemic, high-impact incident from unrelated urgent-looking records and routine questions.
- Evaluate customer, commercial, operational, and potential compliance impact.
- Separate confirmed facts from hypotheses and avoid unsupported regulatory conclusions.
- Recommend a practical response involving engineering and customer-facing or operational stakeholders.
- Respect the read-only boundary and accurately describe what it did and did not do.
This is what makes the task strategic rather than a predefined L2 workflow: the investigation path is open, but the final result must still be objectively judgeable.
Designing an open path with a closed check
One of the most interesting challenges was making the task flexible without making evaluation subjective. I approached this as an “open path, closed check” design.
The agent is free to choose its tools, queries, sequence, and response structure. The verifier, however, checks a defined set of required and weighted outcomes. Required criteria cover the central incident, cross-system correlation, affected customers, business and operational risk, an appropriate coordinated response, evidence grounding, and compliance with read-only constraints. Weighted criteria reward stronger prioritization, richer business context, practical next steps, and a clear distinction between facts and hypotheses.
The criteria judge the result rather than demanding a particular chain of tool calls. That matters because two capable agents may reach the same defensible conclusion using different search strategies.
Making the task durable and safe
Benchmark tasks can become unreliable if they depend on the wall clock, live external data, unstable identifiers, or state mutations. I designed the contribution around three durability properties:
- Synthetic, seeded data: the evidence lives inside the benchmark environment and is safe to publish.
- Stable end-state judging: the final prioritization and recommendation can be evaluated consistently across runs.
- Read-only access: the task tests investigation and coordination while explicitly rejecting false claims that the agent changed external systems.
The pull request also includes the task manifest, container environment, evaluation criteria, test script, reference trajectory, and dataset registration required by the repository.
Validation and review feedback
I ran the repository validation command, which passed linting, task-structure checks, dataset registration, digest integrity, canary requirements, and executable-permission checks. The maintainer confirmed that the contribution has the complete required structure and is a valid task contribution.
The review also surfaced two practical next steps. First, several concurrent contributions selected the same cross-l3-a task directory, so the tasks need distinct names before they can all be merged. Second, this task still needs an end-to-end benchmark run and the deeper L3 review for leakage, criteria quality, and time durability.
That feedback is a useful reminder that a valid task structure is only the beginning. A benchmark contribution also has to coexist cleanly with parallel contributions and behave correctly when an actual agent runs it.
What I learned
1. Strategic tasks still need deterministic evaluation
An open-ended prompt does not require vague grading. The task can allow multiple investigation paths while defining precise properties of a successful outcome.
2. Cross-system correlation is different from retrieval
Finding a support record or engineering issue is not enough. The agent must establish whether the records describe the same underlying business situation and explain why the connection matters.
3. Prioritization needs distractors
If the only visible signal is the target incident, the task measures lookup rather than judgment. Competing high-priority and routine signals force the agent to compare evidence and justify its prioritization.
4. Safety includes honest descriptions of action
An agent can produce a technically plausible answer and still fail if it claims to have performed actions that its tools could not perform. Evaluating this explicitly makes the task closer to real enterprise deployment requirements.
5. Good criteria reward calibrated reasoning
The best response should distinguish confirmed evidence from possible explanations. In operational or compliance-sensitive scenarios, unsupported certainty is a defect, not a sign of confidence.
6. Repository integration is part of benchmark engineering
Naming collisions, manifests, digests, permissions, and executable tests may look secondary to task design, but they determine whether a community contribution is reproducible and mergeable.
Conclusion
My contribution explores a difficult question for enterprise agents: can an agent independently investigate a messy, cross-functional operational situation and turn scattered evidence into a prioritized, safe, and useful response?
Building the task showed me that benchmark design is a balance. The agent needs enough freedom to demonstrate strategic reasoning, while the evaluator needs sufficiently concrete criteria to produce repeatable results. It also reinforced that enterprise-agent quality is not only about finding the right answer. It includes selecting the right evidence, respecting permissions, calibrating claims, and recommending action that humans can trust.
You can review the proposed task and its discussion in pull request #50, and learn more from the Enterprise-Bench repository.
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