The moat isn't the models.
It's the world around them.
Creating an agent is complex — months of development and high costs. Interactor AgentOS provides the underlying features premade for any agent use case.
Replicate the company's world and make it infinitely scalable.
A company is comprised of what it knows and what it can do.
Knowledge flows in from everywhere — and never stops updating.
↓ INGESTED, CONTINUOUSLY
User knowledge — how this person works
User Knowledge Base: their patterns, playbooks, and expertise, learned per user
Organization knowledge — how the company works
Shared definitions, rules, and process, common across every role
Self-learning. Your people's knowledge never stands still — so every conversation and action updates the model. The agent you ran last year is your most experienced one.
Acting means reaching outside — safely.
↑ TWO GATES BEFORE ANY ACTION
1 · Knows how to interact
Service Knowledge Base: what each service can do and how to call it — discovered and learned, no MCP required
2 · Is authorized to interact
Security & Identity: OAuth and tokens brokered per user — the agent acts only with that user's permissions
Self-governing. Knowing how is never enough — every action passes an authorization check first, and every credential resolves just-in-time, per user.
Deep dives: User Knowledge Base · Service Knowledge Base · Security & Identity
Because your agent is unique.
Every team has its own unique set of requirements for its agent. Generic models provide out-of-the-box agentic features, but they fall short of the end goal. Agents built on Interactor AgentOS have the flexibility to fully customize for specialized needs — always an enabler for your agent app.
User Knowledge Base
self-learningRecords what each user does, detects repeating patterns, and turns them into playbooks and a per-user expertise model. The agent you ran last year is your most experienced one.
Service Knowledge Base
self-healingThe registry of external services — auth configs, capabilities, semantic search — backed by autonomous discovery. Query a service we've never seen; it gets learned, not hard-coded.
User Database
one data planeRuntime-defined tables with zero migrations, plus adapters into your existing Postgres, MySQL, HTTP APIs, and S3 — queryable in natural language, with learned semantic mappings.
Agent Orchestration
the runtime loopStateful agent rooms, bounded agentic loops, multi-agent delegation, and durable state-machine workflows that halt for human approval and survive restarts.
Security & Identity
SSO, tokens, OAuthFour-tier multi-tenant identity with JWKS-verified JWTs, plus a per-end-user credential broker: your users connect their own accounts; your app never touches a token.
Billing
usage-meteredMetrics, plans, subscriptions, and adaptive limits with Stripe Connect payout routing — including a vendor API so your customers can bill their own users.
Your app focuses on the business outcome. AgentOS does the heavy lifting.
Solution apps authenticate with app or user JWTs, verified locally against the identity layer's JWKS — no per-request auth callback. Agents discover tools through SKB, act with credentials brokered per end user, remember through UKB, read and write through UDB, and every action is metered through Billing.
ONE AGENT TURN, END TO END
Your user connects an account — one initiate call returns a redirect URL; the OAuth dance, token exchange, and encrypted storage happen entirely on the platform, keyed to your external_user_id.
A request arrives — your app posts a message to an agent room. A per-room process serializes concurrent requests — no races, no double-execution.
Context assembles — the agent's prompt merges account → context → user profiles, conversation memory, the user's connected services, and their learned playbooks from UKB.
Tools are discovered, not hardcoded — semantic search over SKB finds the right capability by intent. Unknown service? Discovery reads its docs and registers it — auth config held to a 100%-correct bar.
Execution is governed — credentials resolve at call time and never enter conversation data. Retries with backoff, loop detection, and idempotency keys keep non-deterministic plans deterministic in effect.
Humans stay in the loop — workflows halt at approval states, persist everything, and resume days later — surviving deploys in between.
Learning compounds — executions feed playbook detection and capability reliability scores. Usage events feed metering. The platform is better tomorrow than it was today.
Autonomy you can actually sign off on.
Multi-tenant by construction — org-scoped JWTs, tenant-partitioned storage, and isolation checks on every externally-parameterized code path.
Credentials never leak — AES-256-GCM envelope encryption, tokens never returned to apps, never logged, resolved just-in-time and discarded.
Deterministic over non-deterministic — structured plans, bounded reevaluation, loop detection, classified retries with backoff, idempotency keys.
Durable by default — workflow state persists at every transition; halted approvals survive restarts and resume cleanly.
Model-agnostic — one inference interface across providers; swapping models is a registry change, not a rewrite.
Audited end to end — every credential access, delegation, state transition, and billing event lands in an append-only record.
Zero-downtime evolution — expand/contract migrations across every service; user-level schema changes need no migrations at all.
Deploy it your way — fully hosted, customer-deployed, or hybrid — run the engine on your infrastructure with credentials managed centrally.
Everyone else's AI starts from zero every morning. Ours learns your business.
Build on the services every agent product needs and nobody wants to build twice. Already in production, already compounding.