Quick answer: HashiCorp Vault is infrastructure-grade secret storage — dynamic secrets, broad ecosystem support, self-hosted control. Gazebo is the agent-facing access and approval layer that sits in front of those secrets. They solve different parts of the same problem and compose cleanly: Vault as the backing store, Gazebo as the per-agent policy and execution layer.
What Vault handles
Vault is one of the most complete secrets management systems available for teams that control their own infrastructure:
Dynamic secrets. Instead of storing a static database password, Vault generates a short-lived credential on demand — a Postgres role that expires in 30 minutes, an AWS IAM key scoped to one action. For infrastructure-level access, this is powerful.
Broad ecosystem. Vault has secrets engines for AWS, GCP, Azure, databases, Kubernetes, PKI, SSH, and more. If you're managing infrastructure credentials, there's almost certainly a Vault integration for it.
Self-hosted control. Vault runs on your infrastructure. For compliance-heavy environments (SOC 2, FedRAMP, heavily regulated industries), keeping the secrets store fully internal matters.
Audit logging at the vault level. Every read, write, and lease is logged. You know when a credential was retrieved and by what path.
If your team is managing infrastructure-level credentials across AWS, Kubernetes, and databases — Vault is built for that problem.
What the agent access layer adds
Vault was designed as an infrastructure secrets store. When autonomous AI agents enter the picture, a separate layer becomes necessary to handle concerns that secret storage wasn't designed for:
Pre-execution approval gates. A secrets store issues credentials. An agent access layer intercepts proposed actions before they execute — letting a human review what the agent is about to do and approve or reject it before any real API call runs. These are different problems at different points in the workflow.
Action-level audit trails. Vault logs credential retrieval at the infrastructure level — which path was read, by which entity, at what time. An agent access layer logs what the agent did with the credential after retrieval: which endpoint it called, what the parameters were, what was approved. Both records are useful; they answer different questions.
Cross-service agent profiles. Vault's policy model is path-based: it controls which entities can read which secret paths. A policy that spans multiple services — "this agent can access Stripe, GitHub, and Vercel, but needs approval before any write operations" — is a higher-level construct that sits on top of path policies. Teams that need this kind of unified agent profile typically build it as a separate layer rather than encoding it into Vault policy directly.
Gazebo provides this layer. It authenticates to Vault as a single service identity, enforces per-agent policies and approval gates, and logs at the action level — adding agent-facing controls without changing how Vault is operated.
This is stack composition, not a replacement decision
“Vault versus Gazebo” can imply that a team must select one product for one job. In practice, the useful comparison is between layers. Vault is the source of truth and control plane for secrets that infrastructure and services need. Gazebo is an agent-facing control plane for deciding whether a named agent may use a connected service for a particular action. A team can use either layer alone where its problem is limited, but teams with both infrastructure secrets and autonomous workflows commonly use both.
That distinction keeps the boundaries clear. Vault retains responsibility for encryption at rest, secret engines, authentication methods, leases, namespaces or mounts, replication topology, and low-level policies. Gazebo retains responsibility for the identity and profile assigned to an agent, the allowed services and operations, approval-required decisions, and action context. Neither layer should impersonate the other: putting every agent decision into a Vault path policy makes policy hard to review, while storing infrastructure bootstrap secrets in an agent profile makes deployment security weaker.
The same credential can participate in both layers without becoming a shared agent credential. For example, a production GitHub integration token can remain at a restricted Vault path. Gazebo uses a narrowly scoped Vault identity to retrieve the backing value only after it authorizes an agent's requested GitHub operation. The agent uses its Gazebo identity, not a Vault token or a secret path. If the agent is later disabled, the Gazebo profile can be revoked without rotating a token still used by an ordinary deployment integration.
Compare the access-control models directly
Vault policies are excellent at protecting secret paths and API capabilities. They can express that a machine identity may read kv/data/production/payments, request a database role, or issue a certificate from a named PKI role. That is a strong infrastructure boundary. The policy evaluation occurs when Vault receives a request, and Vault's audit device records the authenticated entity, request path, operation, and response metadata according to its configuration.
An agent policy begins from a different question: which actor is proposing this operation, for which task, in which environment, and does the operation require a person to approve it now? “The release-notes agent may create a pull request in one repository but may not merge it” is not naturally a secret-path rule. It joins a service operation with an agent identity and an execution decision. “The billing assistant can prepare a refund but a human must approve the refund” similarly requires a gate before the external call, not merely permission to read a Stripe key.
This is why a Vault token should not be handed to individual agents. Even a policy scoped to one path exposes Vault's interface and turns a path read into the effective authorization event. It also makes per-agent revocation, task context, and downstream action attribution harder. A brokered pattern lets an agent ask for an allowed capability while the backing credential remains controlled by the storage layer. The broader reasoning is covered in IAM for AI agents and what a secrets broker does.
A reference deployment boundary
A practical production arrangement has three identities, not one. First, the application or worker that hosts the broker has a Vault workload identity, commonly an AppRole, Kubernetes auth role, or cloud workload identity. That identity is granted only the Vault paths needed to resolve connected credentials. Second, each AI agent has its own Gazebo access profile and token. Third, the external provider has its own credential or delegated authorization with scopes appropriate to the integration.
Keep the Vault identity separate from agent identities. It is normal for Vault audit records to show the Gazebo service identity on a backing-secret read; the agent name belongs in Gazebo's action record. Correlate the two with a request or task identifier rather than trying to manufacture a Vault principal for every transient agent run. This preserves a manageable Vault policy surface while maintaining meaningful agent attribution.
Use environment-specific boundaries at both layers. A staging agent profile should resolve staging credentials through a staging-scoped Vault role, not select production by changing a path parameter supplied by the agent. Give the integration separate configuration and explicit allowlists for production operations. This matters most when an agent can work across GitHub, deployment tooling, and payment systems in one task: a broad “production” role is convenient until an injected or malfunctioning workflow uses a valid credential in the wrong place.
Operational considerations for a combined stack
Start by inventorying which values are true infrastructure secrets and which external-service credentials will be exercised by agents. Record the Vault path, owning team, rotation owner, TTL or rotation process, intended environment, and the Gazebo service connection that depends on it. The inventory prevents a common migration mistake: an integration appears connected, but the Vault policy cannot read its path in production or the secret format does not match what the service expects.
Grant the Gazebo Vault identity the smallest possible policy. It normally needs read on explicit backing paths, not permission to list a mount, administer auth methods, write arbitrary values, or read unrelated application configuration. Separate policies for each environment and integration group make review and emergency containment easier. If a dynamic secret is used, ensure the lease duration accommodates the proposed action and that renewal behavior is understood. A token expiring halfway through a long agent task should result in a clear, retriable authorization failure rather than an invisible fallback to a static superuser credential.
Authentication is a deployment decision. Kubernetes workloads can use a dedicated service account bound to a narrow Vault Kubernetes auth role; VM or managed compute workloads can use an appropriate cloud identity method. AppRole is viable where those methods are unavailable, but protect the secret ID with response wrapping, an expiration, and a use count sized for the actual rollout pattern. Do not place an AppRole secret ID in an agent prompt, source repository, or broadly readable environment variable. Vault Agent guidance is relevant when the broker or other supporting services themselves need infrastructure secret injection.
Treat availability as a designed dependency. Decide whether Gazebo may authorize an action when Vault is temporarily unavailable and a previously resolved credential exists; for sensitive write actions, the conservative answer is usually no. Define timeouts, retries, and a user-visible error for Vault denials or outages. Monitor Vault auth failures, policy-denied reads, lease-renewal failures, and Gazebo approval queue age separately. One alert should not blur “the agent was denied by policy,” “a human has not approved it,” and “Vault cannot be reached.”
Rotation needs an end-to-end drill. Rotate a non-production backing credential, verify that Vault exposes the new value at the expected path, confirm the integration uses it on the next authorized request, and verify that no plaintext appears in application logs, approval messages, or agent conversation context. Test revoking an individual agent profile separately from rotating the backing secret. Those are different incident actions: the former stops one actor; the latter replaces a secret for every approved consumer.
Approval and audit flows should be explicit
An approval gate is useful only when it is placed before the irreversible action. A good approval record includes the named agent, task or run, target service, proposed operation, relevant resource identifiers, and a concise description of effect. It should not contain the underlying Vault value. Approvers need enough context to decide whether a planned deployment, merge, refund, DNS change, or data deletion is intended, but an approval UI should never become another route for exposing credentials.
Define which actions are automatically permitted, which require approval, and which are blocked. Read-only operations may be suitable for automatic execution, subject to data-sensitivity rules. Mutations with financial, production, deletion, permission, or external-communication impact are reasonable candidates for approval. Blocking is important too: no amount of approval workflow makes an operation appropriate for an agent that should never access it. Revisit these rules after observing actual workflows rather than granting a broad write capability to avoid an occasional interruption.
The audit records are complementary rather than interchangeable. Vault answers: did the Gazebo service identity read this secret path, issue this lease, or receive a policy denial? Gazebo answers: which named agent proposed and performed the downstream operation, what profile decision applied, whether an approver intervened, and what the service result was. Retain and protect both records according to incident-response and compliance requirements. Ensure clocks are synchronized and carry a correlation identifier across broker activity where the implementation supports it; otherwise incident reconstruction becomes a manual timestamp exercise.
How they compose
The combination that works for teams with existing Vault infrastructure:
Vault stores the underlying credentials. Your Stripe secret key, your GitHub token, your Vercel API key — Vault keeps them, rotates them if needed, and manages their lifecycle. Vault's dynamic secrets can generate scoped database credentials for infrastructure agents.
Gazebo sits in front of Vault as the agent policy layer. When an agent needs Stripe access, it presents its Gazebo access profile. Gazebo checks the policy, fetches the credential from Vault (or from direct storage), and returns either a scoped credential or a denial. The agent never knows whether the backing store is Vault, 1Password, or anything else — it only talks to Gazebo.
Approval gates live in Gazebo, not in Vault. Before a destructive action, the agent's workflow pauses and waits for human approval in Gazebo's UI. Vault doesn't need to know this happened — it just responds to the credential request after approval.
Audit trails span both layers. Vault logs the retrieval. Gazebo logs what the agent did with the credential — which endpoint it called, what the response was, and whether the action was approved. The full picture lives in Gazebo's audit log; Vault's log is a deeper technical record underneath it.
What to use when
Vault alone is sufficient for:
- Infrastructure credentials where the "agent" is a service account or CI pipeline, not an autonomous AI agent
- Teams with strict compliance requirements for self-hosted secret storage
- Dynamic secrets for short-lived database access, AWS credentials, or Kubernetes service accounts
- Environments where you control the full infrastructure and the agent's scope is tightly bounded
Gazebo adds value when:
- Autonomous AI agents (Cursor, Claude Code, n8n, custom agents) need access to multiple services across a task
- You need plan-then-approve execution before an agent makes real API calls
- You need per-agent audit trails at the action level, not just at the credential-retrieval level
- You want to revoke one agent's access without rotating the underlying credential in Vault
- Non-infrastructure credentials (Stripe, GitHub, Vercel, Resend) need the same policy treatment as infrastructure secrets
For teams already running Vault: Gazebo doesn't replace it. Gazebo can pull from Vault as the backing store while adding the agent-specific policy and approval layer on top. The integration page for HashiCorp Vault covers the setup in detail.
For teams not running Vault: Gazebo's built-in credential vault handles the storage layer directly, so you don't need to run Vault infrastructure just to add agent access controls.
How Gazebo+Vault works in practice
For a team already running Vault, the integration doesn't require migrating credentials or changing how Vault is operated.
Step one: Gazebo authenticates to Vault. Gazebo connects to your Vault cluster using an AppRole or a Kubernetes service account — the same auth methods you're already using. This is a single connection, managed once. Your existing Vault policies don't need to change.
Step two: credentials stay in Vault. Your Stripe key, GitHub token, and Vercel credential remain in Vault at whatever paths they're already stored. Gazebo doesn't copy them out or cache them. When a credential is needed, Gazebo fetches it at request time.
Step three: agents get Gazebo access profiles, not Vault credentials. Each agent is assigned a Gazebo profile that declares which services it can access and under what conditions — free access, approval-required, or blocked entirely. The agent presents a Gazebo-issued token. It never sees a Vault token, an AppRole ID, or a secret path.
Step four: Gazebo resolves the credential at execution time. When the agent's workflow reaches a step that needs Stripe access, Gazebo checks the profile policy, checks whether approval is required, and — if the action is cleared — fetches the credential from Vault and returns it to the agent for that call. If the action requires approval, the workflow pauses. The Vault read happens only after a human approves the step.
Step five: audit trails merge. Vault logs the credential retrieval with Gazebo's service identity. Gazebo logs the full action context — which agent, which task, which endpoint was called, what the approval chain looked like. You get two complementary records: Vault's cryptographic audit log at the infrastructure layer, Gazebo's action-level log at the agent layer.
The operational result: Vault's topology doesn't change. No new secret paths, no restructured policies, no new AppRoles per agent. The agent-facing complexity — access profiles, approval gates, per-agent audit trails — moves into Gazebo.
The underlying distinction maps to what IAM for AI agents covers in more depth: secrets storage (where credentials live) and access policy (what an agent is allowed to do with them) are two separate problems that call for two separate layers.
For a closer look at what Vault Agent specifically does and how it fits into this picture, see what is a vault agent. For the full credential lifecycle from provisioning through revocation, see AI agent credential management.
This is an independent editorial post — not affiliated with or endorsed by HashiCorp. Last reviewed: August 2026. Check HashiCorp's current documentation for the latest Vault capabilities and configuration options.