SYS:SOLUTIONS // AI Agents
Authorization that keeps pace with autonomous agents
Every agent action — every tool call, every document retrieval, every API request — needs a permission check scoped to the invoking user. Traditional authorization systems buckle under this workload. InferaDB handles dozens of checks per agent turn with no perceptible latency, full delegation modeling, and a complete audit trail.
Agent authorization is a fundamentally different problem
A human clicks a button. An authorization check fires. Simple. An AI agent reasons through a multi-step plan — retrieving documents, calling tools, making API requests — and every one of those actions must be authorized against the permissions of the user who invoked the agent. When your authorization layer adds 5-50ms per check, you have two choices: make agents unusably slow, or skip the checks entirely and pray.
What happens without proper agent authorization
Most teams today give agents broad service-account permissions and hope the prompt engineering holds. It will not. An agent with overly broad access will eventually retrieve documents the requesting user cannot see, invoke tools the user is not authorized to use, or take actions across tenant boundaries. The result is not a bug report — it is a breach disclosure, a regulatory finding, or both. The question is not whether it will happen, but whether you will know when it does.
Dozens of checks per turn
An agent reasoning over a user's documents might check permissions on 20+ resources in a single turn. Traditional policy engines turn this into 100ms+ of blocking latency per turn.
User-scoped delegation
Agents act on behalf of users, not as autonomous principals. Every action must be constrained to the delegating user's access — not the agent's service account.
Auditability requirements
When an agent accesses sensitive data, you need to prove exactly why it was allowed. "The AI did it" is not an acceptable answer for compliance or incident response.
The agent acts as the user — not beside the user
Delegation modeling is the conceptual foundation of secure agent authorization. When a user invokes an agent, that agent should inherit exactly the user's permissions — no more, no less. InferaDB models this as a first-class relationship in its authorization graph, so every downstream action is evaluated against the delegating user's actual access.
Concrete example: delegation in action
User Alice invokes an agent. The agent decides to call Tool X, which reads from Database Y. InferaDB evaluates two checks: can Alice use Tool X? Can Alice access the specific records in Database Y that Tool X would touch? Both must pass. If Alice's access to Database Y was revoked five seconds ago, the agent is denied immediately — not on the next token refresh, not eventually. Immediately.
Transitive permission checks
InferaDB traces the full delegation chain — user to agent to tool to resource — and verifies permissions at every link. No implicit trust between layers.
No privilege escalation through indirection
An agent cannot access resources that its invoking user cannot access. Period. The delegation model makes escalation structurally impossible, not just policy-forbidden.
Real-time revocation
Revoke a user's access and every agent acting on their behalf loses that access immediately. Not eventually. Not on the next sync cycle. Immediately.
Retrieval-augmented generation that respects access control
RAG without authorization is a data leak waiting to happen. When your agent retrieves documents to answer a question, InferaDB ensures it only sees documents the requesting user has access to. No vector store post-filtering. No prompt injection through unauthorized context. No hope-based security.
Pre-retrieval authorization
InferaDB checks document-level permissions before retrieval, not after. Your vector store never returns results the user should not see — eliminating the window where unauthorized content could influence the agent's reasoning.
No context window contamination
Post-filtering is not enough. If an unauthorized document enters the context window — even briefly, even if you strip it before the response — the model has already seen it. Pre-retrieval authorization eliminates this class of leaks entirely.
Cross-tenant RAG isolation
In multi-tenant environments, RAG scoping ensures one tenant's documents never enter another tenant's agent context. InferaDB enforces this at the authorization layer, so your retrieval pipeline does not need to be tenant-aware.
Fine-grained control over every tool invocation
Every tool an agent can invoke is an authorization decision. InferaDB lets you model which tools each agent type can access, scoped to the delegating user's permissions. An agent with access to the "send email" tool can only send as the user who authorized it — and only to recipients that user has permission to contact.
Tool-level permissions
Define which tools each agent type can access using IPL. Restrict sensitive operations — delete, transfer, escalate — to specific agent configurations and user permission levels.
Parameter-level constraints
Authorization goes beyond "can this agent call this tool." InferaDB can constrain the parameters a tool is called with — limiting which accounts, resources, or scopes the tool operates on.
Framework compatibility
Works with LangChain, CrewAI, AutoGen, and any agent framework with middleware support. Wrap your tool executor with InferaDB middleware and every invocation is authorized automatically.
Full audit trail for every agent action
Every authorization decision InferaDB makes includes a complete resolution trace — which relationships were traversed, which conditions were evaluated, why the decision was allowed or denied. When an agent accesses sensitive data, you can reconstruct the exact permission path that permitted it. When an agent is denied, you can see precisely why.
Permission resolution traces
Every check returns the full graph traversal — from user to delegation to tool to resource. Compliance teams can audit any agent action without reverse-engineering the permission model.
Deny explanations
When an agent action is denied, the resolution trace shows exactly which permission was missing. Debug authorization issues in minutes, not hours of log spelunking.
Compliance-ready logging
Every agent action — allowed or denied — is logged with full context: the invoking user, the agent identity, the tool called, the resources accessed, and the complete resolution path. Ready for SOC 2, HIPAA, and regulatory review.
Three steps to authorized agents
InferaDB integrates with your agent framework as middleware. You do not need to rewrite your agent logic or change your tool implementations.
1. Wrap your agent framework
Add InferaDB middleware to your agent executor. Every tool call and retrieval request is intercepted and authorized before execution. Works with LangChain, CrewAI, AutoGen, and custom frameworks.
2. Define tool permissions in IPL
Use InferaDB's Permission Language to model your agent's tool permissions, delegation relationships, and resource access rules. Express complex authorization logic declaratively.
3. Every action is authorized and logged
Once integrated, every agent action is automatically checked against the delegating user's permissions and logged with full resolution context. No code changes to your tools or agent logic.
AI agents are shipping today without proper authorization.
Do not be the breach headline.
Delegation modeling, per-user RAG scoping, tool authorization, and full resolution traces for every agent action — with no perceptible latency overhead.