Why teams implement IntentGate
Five concrete benefits, from compliance evidence to engineering productivity. Each one describes what changes for the team and how IntentGate delivers it. A calculator at the bottom of the page lets you model the impact in your own environment.
Compliance and audit readiness
Produce per-agent evidence on regulator demand in seconds, not weeks.
Every tool call routed through IntentGate writes one record into a tamper-evident, hash-chained store. The record carries the agent, the user, the policy version in force, the inputs, the decision, the timestamp. When the EU AI Act, GDPR or NIS2 reviewer asks for everything agent X did on a given day, the export is a single signed query.
The team it replaces is the squad that today correlates CloudTrail, SaaS audit APIs, IdP logs and model-vendor logs by hand. Where that work takes weeks of senior security and legal time, IntentGate turns it into a query and a verification command. Regulators are starting to treat "we could not produce the records" as a control failure on its own, which is the failure mode IntentGate exists to eliminate.
- Per-call decision record with full attribution: agent, user, policy version, inputs, decision, timestamp.
- Tamper-evident hash chain with one-command verification.
- Signed export to regulator in a single query.
- Standardized error logs (OCSF-aligned, stable JSON-RPC error codes) ready for SIEM.
- Plain-intent recording: what the agent was trying to do, not just the syscall it produced.
Security and risk reduction
An authenticated agent with valid credentials can still be prompt-injected into taking actions the user never asked for. IntentGate refuses those at the moment of execution.
Each call is evaluated by five checks: capability (did we issue this token), intent (does this match what the user actually asked), policy (does Rego permit this), budget (is the agent inside its allowance), memory provenance (are the inputs the agent is acting on authentic). The first failure short-circuits the rest. The agent never reaches the upstream tool.
This is what closes the gap between IGA, PAM, DLP and the actual decision the agent is about to make. Those layers govern who an actor is, what they can theoretically access, and what data must not leave. None of them look at the combined agent-plus-user-plus-action-plus-now picture. IntentGate is that combined picture.
- Prompt-injection mitigation: intent check refuses calls whose content does not match the user's actual request.
- Least privilege per call: capability tokens scope an agent to specific tools, tenants and time windows.
- Human-in-the-loop for destructive verbs: JIT approval, step-up MFA, and explicit escalation before refunds, deletions or external sends.
- Memory provenance: calls based on tampered RAG or memory inputs are refused with the offending entry named in the audit row.
- Blast-radius containment: one compromised agent cannot exercise its full credential scope when policy gates the destructive subset.
Centralized control over agent decisions
One decision point, one policy store, one audit log for every agent the organization runs.
Today the policy that controls what an AI agent can do lives in fragments. Some grants are in Salesforce, some in Microsoft 365, some in the bespoke code an application team wrote, some not written down anywhere. The composite picture exists in no single system. The CISO cannot answer "what can this agent do" without a multi-week project.
IntentGate concentrates the policy into one Rego file, the audit into one chained log, and the operational view into one console. Changing what every agent in the estate is allowed to do becomes an edit to a policy file, with the change appearing in the audit log alongside every decision that policy version produces.
- One Rego policy file controls every agent in the estate.
- One audit log holds every decision, queryable by agent, user, tenant or time window.
- Policy version pinned per row: every audit entry names the exact rules that were in force at the moment of the decision.
- One operator console shows what every agent can do, with whom, and against which tools.
Operational efficiency for security operations
Incident response shortens because the attribution chain is one query, not a forensic correlation exercise.
When an agent does something that warrants investigation, the IR analyst opens the audit log and queries by agent, time window and user. Every row already carries the policy version, the inputs the agent was acting on, and the decision the gateway made. The chronology is already chronological. The attribution is already attributed. The questions the analyst asks are the columns of the table.
The cost: the ops team operates the gateway and authors policy in Rego. The benefit: investigation time drops from the hours currently spent reconstructing across systems to the minutes needed to read the answer. Multiplied across a year of agent incidents in a large enterprise, this is one of the most tangible operational gains the product produces.
- Attribution is a column on the audit row, not a forensic reconstruction.
- Investigation closes in minutes when the chain already lines up by time, agent, user and policy version.
- Standardized JSON-RPC error codes feed SIEM rules and SOC dashboards directly.
- Real-time webhook fan-out to Slack, Teams and PagerDuty on configurable triggers.
Engineering productivity per agent deployment
Application teams stop writing bespoke per-agent authorization, scope and audit code. The SDK does it.
Every team shipping an AI agent today writes the same three things: a layer that checks whether the agent should be making this call, a layer that scopes the call to the right tenant, and a layer that logs the call somewhere durable. Each implementation is slightly different, none is reviewed, and none produces evidence in a format anyone else can read.
IntentGate ships Python and TypeScript SDKs that wrap the tool-call interface. The agent code declares the policy it needs and the SDK handles capability tokens, scope enforcement and audit emission. The work that used to be tens of engineering hours per agent becomes configuration. The production code becomes shorter, easier to review, and consistent across deployments.
- Python and TypeScript SDKs wrap the tool-call interface; zero runtime dependencies.
- Capability attenuation primitive built into both SDKs for sub-agent delegation.
- Audit emission handled by the SDK; agent code never writes its own logging.
- One codebase per agent shrinks; reviews and security audits scale linearly across the estate.
Runtime cost and resource control
Off-context tool calls and runaway agent loops are caught at the gateway, before they consume tokens, backend cycles or money.
An autonomous agent that misreads its own context can spiral. It calls the same tool fifty times. It loops on a query that never converges. It tries a destructive verb the operator never sanctioned. Without a control point, the bill arrives at the end of the month: token spend at the model provider, API quota burned at the downstream service, on-call hours chasing the loop.
IntentGate's budget check refuses the N+1th call when the agent has burned its per-hour or per-day allowance. The policy check refuses calls outside the sanctioned tool set regardless of how the agent arrived at them. Both stop upstream of the model and the downstream tool, which means the unauthorized call never costs tokens, never adds backend load, never lands in a vendor bill.
- Per-agent and per-tenant rate and cost ceilings; the 101st call this hour is refused.
- Off-context tool calls refused before they reach the model, eliminating wasted token spend.
- Refused calls never reach the downstream tool, reducing backend API load and bills.
- Predictable per-tenant cost envelopes so finance can plan spend.
Run the math on your own environment
Two panels show the annual exposure on the table for IntentGate to operate on. Panel A is operational exposure (the hours and euros the team currently spends). Panel B is risk exposure (expected loss from agent incidents and regulatory findings). Edit any input and the totals update.
Annual operational exposure
Hours and euros the team currently spends on the work IntentGate operates on. This is what is on the table for the gateway to address.
1 · Audit reconstruction
When a regulator or internal audit asks for per-agent evidence, today's team correlates timestamps across CloudTrail, SaaS app logs, IdP logs and vendor APIs. IntentGate produces the signed export from a single hash-chained table.
Requests × hours × rate
2 · Incident response
Agent-related investigations consume hours stitching attribution from log fragments. With the gateway audit chain, the same attribution is one query.
Incidents × hours × rate
3 · Bespoke agent authorization code
Building per-agent authorization, scope and audit logic in-house takes engineering hours. Some teams do this work; others ship agents without it and accept the risk. The row shows what that work costs, either what the team currently spends or what doing it properly would cost without the gateway.
Deployments × hours × rate
Annual risk exposure
Expected-value math. These numbers represent the risk-adjusted exposure on the table for the gateway to address. They are not cash you will see on a P&L line.
4 · Agent over-privilege blast radius
A prompt-injected agent can exercise every credential it holds. IntentGate refuses out-of-scope tool calls, content-mismatched intents, and policy-violating destructive verbs at the moment of execution. Enter the number of significant agent-related security events the organization expects in a year and the average cost; the row shows the annual expected exposure.
Incidents/year × avg cost
5 · Regulatory penalty exposure
EU AI Act high-risk non-compliance: up to 3% of global revenue. GDPR: up to 4% (or €20M minimum). NIS2: up to 2% (or €10M minimum). IntentGate produces the per-decision evidence on demand, which is the artifact regulators ask for.
Revenue × (fine% / 100) × (probability / 100)
Ready to model your own environment?
The defaults above are illustrative. The conversation starts when you change them to your numbers. Bring the calculator output to a 30-minute call and we will walk it through with you.
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