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Why a prompt is not a complete delivery contract
A prompt is part of an agent’s behavior, not a full account of the system around it. It may set goals, constrain responses, or describe how to use tools, but it does not automatically give teammates a reviewable change history, tell another application how to connect, or preserve the origin and verification status of facts passed between components.
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AWS Prescriptive Guidance describes prompt management as an engineering discipline: “Prompt management throughout the AI solution lifecycle has evolved from an ad-hoc craft into a critical engineering discipline.” In practice, that means treating prompts as maintained assets rather than one-off text. Teams can manage them in a prompt registry or use a prompts-as-code approach with Git, so changes can be reviewed and maintained through an existing development workflow. That workflow supports accountability; it does not replace scenario-specific review, threat modeling, or validation.
Use each artifact for the job it can actually do
A README is principally for people who need an overview of a project. A prompt governs behavior in context. An Agent Card is a machine-readable discovery and connection artifact. Other transparency and provenance records address different questions. These artifacts can complement one another, but they are not interchangeable and none is a general-purpose guarantee of trust.
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| Artifact | Primary audience and purpose | What it can communicate | What it does not establish on its own |
|---|---|---|---|
| README | People evaluating or working with a project | Project overview and human-oriented context | Machine-readable agent discovery, verified claims, or secure runtime behavior |
| Prompt or prompt set | The model and the team maintaining its instructions | Behavioral instructions and task framing; a registry or Git workflow can make changes maintainable | That the instructions are correct, that tool access is safe, or that generated claims are true |
| A2A Agent Card | Software discovering or connecting to an agent | Identity, capabilities, authentication requirements, and supported skills, as described by FINOS | That declared capabilities match actual behavior, or that the agent is safe or truthful |
| Provenance or trust metadata | People and systems evaluating information across handoffs | Whether a fact is merely asserted, deterministically attached by framework code, or cryptographically attested | That an assertion becomes verified merely by being repeated or summarized |
| Transparency or governance card | Users and stakeholders assessing an application or platform | Capabilities, intended uses, limitations, or governance-oriented details, depending on the card | Universal fields or a common standard across distinct card formats |
GitHub’s Application and Platform Cards are transparency resources describing capabilities, intended uses, and limitations. FINOS describes its financial-services Agent Card extension as adding governance-oriented fields to a card whose main role is discovery and interoperability. Those cards have overlapping transparency aims, but should not be treated as the same format or as universally required fields.
What an A2A Agent Card does—and does not do
FINOS describes an Agent Card as a JSON document published by an agent to communicate identity, capabilities, authentication requirements, and supported skills. The card is retrieved from the well-known path /.well-known/agent-card.json. Microsoft Learn demonstrates a framework resolving a card and creating an agent from it. This makes the card useful when a compatible application needs a structured way to learn how to find or interact with an agent.
The distinction is important: a card reports information; it does not independently test the agent against that information. A declaration that an agent supports a skill is not evidence that the skill works reliably, and listing authentication requirements does not prove the surrounding system enforces them correctly. Card fields and framework integrations can evolve, so implementations should follow the current protocol revision and framework documentation rather than assume that a particular example is stable across versions. The FINOS extension’s added governance fields apply to its financial-services context, not automatically to every A2A implementation.
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The Agent Control Standard distinguishes among facts that are asserted, facts attached deterministically by framework code, and facts cryptographically attested through signatures or hash chains. Those categories describe different bases for confidence; they should not be collapsed into a single label such as “verified.” A system should make the basis visible wherever a fact is consumed.
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This matters when an agent summarizes, stores, or hands information to another component. If the original trust basis is dropped, a later reader may mistake an unsupported assertion for an attested fact. Rephrasing, summarizing, or moving a claim across a boundary does not upgrade its status. Design records and interfaces so the origin and verification basis travel with the claim, and ensure downstream components do not imply stronger assurance than the source provides.
Secure the application around the agent
Security is shared between the model-facing instructions and the application that supplies data, credentials, and tools. Microsoft’s secure-agent guidance emphasizes validating inputs, securing data flows, and configuring tools for the specific scenario. It warns that retrieved content can contain hidden instructions intended to change model behavior or influence tool calls. Anthropic likewise notes that greater agent autonomy can expand the scope for misread intent and prompt-injection attacks.
- Treat retrieved material as untrusted input. Content from documents, web pages, or other external sources can contain instructions that conflict with the task. Do not assume that placing a warning in a prompt is sufficient protection.
- Constrain tool access in the application. Configure tools and permissions for the task, and validate inputs and data flows rather than relying on the agent to police its own boundaries.
- Review behavior, not just declarations. Test how the agent handles hostile or irrelevant retrieved instructions and whether tool calls remain within the intended limits.
- Keep human decisions with people. Apache’s draft AGENTS.md guidance makes a useful project-policy distinction: agents can record what they used and report findings, while people retain decisions about rights, copyrightability, and third-party licensing. That draft is not a complete statement of law.
A practical way to ship the artifacts together
- Maintain instructions as code or registered assets. Keep prompt changes reviewable and tied to the lifecycle of the product. Record enough context for maintainers to understand what changed and why.
- Write the human overview for humans. Use the README to explain the project, its intended use, and relevant limitations. Do not expect it to serve as the protocol contract for software integrations.
- Publish a card for discovery. Where an A2A integration applies, publish the Agent Card at the documented well-known path and describe identity, capabilities, authentication requirements, and skills accurately.
- Preserve provenance at handoffs. Carry the trust basis of facts through summaries, storage, and component boundaries so a downstream consumer can distinguish assertions from deterministic or cryptographic support.
- Test the complete application boundary. Exercise untrusted retrieved content, tool permissions, input validation, and data flow in the context where the agent will run. A prompt review or card check alone is not a security review.
How to judge whether the package is trustworthy
Evaluate the pieces against the question each is meant to answer. Can a maintainer inspect how instructions evolve? Can a compatible system discover the agent and understand its declared interaction requirements? Can a recipient tell whether a claim is asserted or supported by a deterministic or cryptographic basis? Are capabilities and limitations described for the intended audience? Are untrusted inputs and tool boundaries handled by the application? A trustworthy package makes these answers inspectable without suggesting that documentation itself proves behavior.
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