Before an agent acts, ask what the record proves.
A capable demonstration is not the same thing as a deployable system. Before a pilot, procurement decision, or integration, turn broad statements about an agent into a bounded evidence record: what it may do, what supports the assertion, what is unknown, and who owns the next decision.
Start from a reader’s decision, not a technology label
“Agentic AI” can describe markedly different systems. An agent that drafts text under supervision raises different questions from one that reads sensitive data, calls production tools, delegates work, or makes an irreversible external request. The useful starting point is therefore a bounded decision: What permission, workflow, or risk would this system introduce if we moved forward?
The Field Index provides public context on standards, projects, and infrastructure. It does not assess a specific vendor or implementation. This guide preserves that boundary and gives a practical way to collect the missing evidence.
The six-part evidence frame
| Ask for | Useful evidence | Question that narrows the claim |
|---|---|---|
| 1. Purpose and scope | A single task statement, intended users, environment, and non-goals. | What decision or action is the system allowed to take, and what is explicitly out of scope? |
| 2. Authority boundary | Tool permissions, approval points, identity/role model, and a list of irreversible actions. | Which actions need human confirmation, and what prevents the system from exceeding that boundary? |
| 3. Data boundary | Data classes, retention, connector list, access path, and handoff rules. | What data may enter the system, leave the system, or be retained—and who can inspect that flow? |
| 4. Failure and misuse model | Threat model, known failure modes, misuse scenarios, and mitigation ownership. | What happens if the agent follows malicious or conflicting instructions, calls the wrong tool, or produces an unsupported output? |
| 5. Evaluation record | Test method, version, representative cases, observed limits, and reproducible results where possible. | What was evaluated, by whom, against which version, and what did the evaluation not test? |
| 6. Stewardship and correction | Named owner, change log, incident/correction path, rollback plan, and recheck date. | Who can stop, correct, or roll back the system—and how will a reader learn that the public record changed? |
What counts as evidence
Evidence is not a polished architecture diagram, a general claim of “responsible AI,” or a benchmark result with no method. A useful record lets a reader locate a versioned source and see the condition it supports. For a public open-source claim, that usually means a repository, licence, version or release, contribution path, and a plain account of known limits. For a measured claim, it means the population, test method, date, version, and a limitation—not only a favorable number.
A first-party document can be valuable when it is explicitly bounded. It becomes less useful when it silently converts a plan into a completed capability, a framework into a universal requirement, or a potential result into an outcome promise.
Use established material as context, not borrowed authority
NIST describes its AI Risk Management Framework as a voluntary resource intended to help organizations incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. Its familiar Govern, Map, Measure, Manage structure is a useful prompt for identifying work that a system owner should make visible; it is not a declaration that a particular deployment is trustworthy or compliant.[1]
OWASP’s Agentic Security Initiative publishes a threat-model-based reference for emerging agentic threats and mitigations. That makes threat modeling and clear ownership valuable evaluation inputs, but a link to OWASP does not constitute a security assessment.[2]
Open projects may clarify how an interface or protocol works. The Agentic AI Foundation’s public project catalogue, for example, links to projects including MCP, A2A, AGENTS.md, goose, and agentgateway. A project listing is not an endorsement, maturity assessment, security review, or compatibility claim for any implementation.[3]
Know when the honest answer is “not yet”
Hold a decision when a material claim cannot be traced, an owner cannot explain a limitation, evidence is stale, or a system’s authorization boundary is unclear. “Not yet” is a useful technical outcome: it keeps a system from acquiring a confident public story before it has a reliable operating record.
When you cannot obtain evidence, do not fill the gap with a broader promise. Narrow the use case, reduce permissions, add a human approval point, or wait for a documented implementation.
Turn the conversation into one working record
The worksheet below captures evidence links, owners, limitations, and the next required decision. It has no submission action or tracking. Use it internally, adapt it to your review process, and preserve any decision with its evidence boundary.
Sources and limits
This guide’s six-part frame is a Coreweaver Field Index proposal. The outside materials below provide context for risk management, threat modeling, and public open-source project documentation. They do not validate a particular product, organization, deployment, or procurement decision.
- National Institute of Standards and Technology, “AI Risk Management Framework.” Checked 2026-08-27.
- OWASP, “Agentic AI — Threats and Mitigations.” Checked 2026-08-27.
- Agentic AI Foundation, “Projects.” Checked 2026-08-27.
Practical next step
Need a narrower evidence question?
If you have a public source, a correction, or a specific evidence boundary that should be represented in the Index, send it with the source URL and the claim it supports. Coreweaver Labs reviews corrections before changing a public record.
Discuss an evidence review