Discover what's already running. Provision agents governed by design. Operate them safely in your own cloud, with cryptographic evidence of every decision.
Every regulated AI project dies in the same three places: no inventory, no enforcement at the point of action, no audit substrate. AgentCore was built to make each one a one-line answer.
Read-only sweeps across AWS and Azure surface every managed and shadow agent. Risk-classified, blast-radius-mapped, in days — not quarters. No changes made to your environment.
Agents provisioned from blueprints where guardrails, identity, risk tier and control mappings are part of the schema. An ungoverned agent is not something AgentCore can produce.
One Gateway per Enclave. Ten gates on every loop iteration — admission, retrieval, inference, tool call, memory write, egress. Policy-as-code in OPA / Rego.
Per-tenant hash-chained, KMS-signed journal written to S3 Object Lock. Exportable as a verifiable audit pack — offline-verifiable by someone who does not trust AgentCore.
Three planes, one control plane, your cloud. Deployed inside your AWS account. Your data and inference never leave the boundary.
Collectors walk model and agent services, serverless, containers, identity and networking for agent signatures. Managed vs shadow surfaced explicitly. Blast radius mapped to data, tools and downstream systems reachable from each agent's identity.
Bedrock, Lambda, ECS, IAM, CodeBuild, VPC, CloudWatch — read-only, no changes made.
Azure OpenAI, Foundry, Functions, Entra, Key Vault, Log Analytics — same read-only sweep.
Framework, pattern, risk level and blast radius inferred for every agent found.
Every agent is provisioned from a blueprint where guardrails, identity, risk tier and compliance-control mappings are structured data — not documentation added later. Readiness scoring ranks gaps before go-live and gates promotion.
Controls-as-schema: guardrail profile, risk tier, framework mappings and readiness score in one definition.
Scoped IAM role issued per agent at provision time. Least privilege enforced at IAM, not by convention.
Terraform / OpenTofu plan → policy gate → approval → apply. Tamper-evident run history.
The Enclave wraps every model call. One Gateway — the single policy enforcement point — is the only path in or out. No side door. A Doctor service continuously health-checks each Gateway and applies safe automated fixes.
Each agent gets its own IAM role, memory namespace, budget cap and guardrail profile.
App request authenticated and admission-checked. Tool calls brokered per-turn. Response PII-scanned before egress.
Built on Bedrock AgentCore runtime, VPC-isolated, PrivateLink endpoints, ECS Fargate.
Real Rego, not config toggles. Authored in source bundle, tested in CI, synced to runtime on deploy. Enforcement modes: dry-run → enforce → disabled. Continuous compliance posture per framework driven by live platform state, not a questionnaire.
Source bundle → CI gate → policy store (versioned, per-tenant) → runtime gates. Fail-safe evaluation.
APRA CPS 234 · CPS 230 · EU AI Act · NIST AI RMF · ISO 42001. Policy ↔ clause both directions.
Continuous weighted score per framework per tenant. For any control: which policy enforces it, current posture, evidence of it firing.
Every governance-relevant event appended to a per-tenant chain. Any alteration breaks the chain and is detectable. Entries signed with per-tenant KMS keys, written to S3 Object Lock, exportable as a verifiable audit pack with Merkle proof + public key + verify.sh.
Per-tenant chain. Tamper breaks the chain downstream — detectable by any outsider without access to the platform.
Per-tenant KMS asymmetric keys. Signed at write time. Verifiable by a third party, offline, forever.
Evidence + Merkle proof + public key + verification script. Everything an auditor needs in a single bundle.
Every agent is budget-bounded and can be halted mid-execution. At cap the agent is HALTED — dropped, not throttled. Distributed tracing end-to-end across Gateway, gates, model calls, tools and memory writes. OpenTelemetry export to your monitoring stack.
Per-agent token + dollar tracking. Threshold alerts on approach. HALT at cap — sealed in the evidence chain.
Daily spend vs rolling baseline. Multiple-of-baseline spike triggers within minutes. Linear + trend-based spend projection.
Every gate decision, budget event and HITL pause is an observable event. OTel export to CloudWatch, Datadog and beyond.
Inventory of AI agents reconstructed from email threads and tickets — quarterly at best
Shared service credential on every action — not attributable to any specific agent
Stop a runaway agent by finding the credential, rotating it, restarting the service
72-hour NDB reconstruction with vendor logs, screenshots and analyst time
APRA CPS 234 coverage answered by a self-assessment questionnaire, point-in-time
Months to promote an agent prototype: security review, risk, finance, rebuild
Monthly bill surprise on long-running agents; throttle after the fact
Continuous, live agent inventory across AWS + Azure — managed and shadow
Dedicated IAM principal per agent, stamped on every evidence entry
Budget cap HALT, stream watcher halt, identity revoke — live and scoped
Hash-chained, signed evidence exported as a verifiable pack — offline-verifiable
Live framework score driven by real platform state — policy ↔ clause map
CLI promote: sandbox shape → production scope in minutes
Per-agent budgets, rolling-baseline anomaly detection, HALT at cap
Book a session with our team. We’ll walk through your current AI footprint, the three blockers most regulated organisations hit, and what a governed deployment looks like in your account.
Read-only inventory of every AI agent running in your AWS and Azure accounts. Risk-classified, blast-radius-mapped.
Custom Enclave architecture, policy framework mapping, and governed blueprint strategy for your estate.
Detailed implementation plan: first governed agent in 14 days, full estate coverage roadmap, framework sign-off path.