Grounded retrieval
Use RAG for narrative evidence and typed knowledge graphs for exact entities and relationships.
For platform and security teams
Enterprise AI agents: grounded, scoped, accountable.
Move beyond demo-ware with an agent runtime designed around data control, exact retrieval, scoped tools, authenticated context, and visible execution.
Production requirements
What enterprise AI agents need to reach users
Security boundaries
Scope tools per agent, forward identity, and require confirmation for consequential operations.
Key handling
Pass per-customer LLM keys by request header — used once, never stored, never logged.
Operational evidence
Trace structured run events, review audit data, and monitor runtime health.
Reliability
Grounded AI agents: answers from your records, not guesswork
The market is full of AI that looks impressive in a demo and then makes things up in front of a customer. SyntheticBrew grounds answers in your data: a knowledge-graph taxonomy gives the agent typed, deterministic retrieval, and RAG covers your documents. The agent answers from your records — and can be instructed to refuse when the evidence is missing.
For done-for-you builds, an eval harness scores every answer for groundedness and correct source attribution on your real data before launch — reliability you can measure.
Governance
AI agent governance: audit logs, scoped keys, security zones
Know what every agent did, and limit what each one can do.
Immutable audit log
Every administrative action is recorded — who changed what and when — for security, compliance, and governance reviews.
Least-privilege API keys
Scope keys to exactly what each integration needs: chat, tasks, agents, config, or admin. Revoke any key instantly.
Tool security zones
Tools are tiered by risk. Dangerous actions require explicit confirmation, and agents see only the tools they are configured for.
Secure by default
Secure AI agents: identity, keys, and billing you never have to babysit
Tamper-proof auth
Every API request is verified against an Ed25519-signed token. Forged tokens, downgrade attacks, and replay attempts are rejected at the door.
No passwords to manage
End users never authenticate against a SyntheticBrew password store — there is none. Sessions are Ed25519-signed tokens; plug in your existing IdP with one public key.
Bring your own LLM keys
Pass per-customer LLM API keys via request headers — used once, never stored, never logged. Clean per-tenant billing at your provider’s list price.
No double-billing, ever
If usage tracking cannot reach the cloud, requests stop — instead of silently double-counting later. Your invoice always matches reality.
Control plane
Separate what users can do from what operators manage
Optional two-port mode splits the runtime into an external data plane — the chat and agent APIs your product calls — and an internal admin plane carrying the dashboard and management APIs, which you keep behind a VPN or firewall.
- External data plane for chat and agent APIs
- Internal admin plane behind your VPN or firewall
- Ed25519-signed JWT authentication
- Scoped API keys and BYOK
- Per-agent tool access with confirmation boundaries
Verifiable up front
How do you evaluate an enterprise AI agent vendor?
Make it prove itself on your data before a contract is signed. Run your own evals against a working agent on the free plan, read the audit log for every tool call it made, and trace each run event by event — so the evidence is your measurements, not our claims.
Questions
Enterprise AI agents: common questions
What makes an AI agent "enterprise-grade"?
Reliability and control. The agent must be grounded in your data, auditable, and scoped to least privilege, with a human checkpoint before consequential actions. SyntheticBrew is built in Go as a production runtime with those properties built in — not a prototyping toolkit.
How do you keep enterprise AI agents grounded instead of guessing?
Grounding. Knowledge graphs give the agent typed, deterministic retrieval over your real records, and RAG covers unstructured documents. You can also instruct the agent to say "I don't know" rather than guess. For done-for-you builds, an eval harness scores every answer for groundedness and correct source attribution before launch.
How do we keep control of our data and model keys?
Your LLM keys are yours: pass them per request and they are used once, never stored and never logged, so inference runs on your provider account at list price. Agents, knowledge, and session history stay queryable and exportable through the API, and every tool call an agent makes is recorded in an immutable audit log.
Do you offer a done-for-you build?
Yes. We deliver a production agent in your platform to a measured bar — accepted against evals on your real data — then hand it over for you to run. Fixed scope, fixed price, and the configuration is yours to keep and change. Book a fit call to scope it.
Evaluate the exact runtime you will operate.
Start on the free plan and let your team test the system against your own data before a production decision.