One engine. Every agent.
Ship, version, and roll back AI agents like software releases. Every prompt, model, and tool change is staged, eval-gated, and audited before it reaches a customer — on your brand, in your region.
One engine, every agent — configured, not coded.
Every new agent used to be a new codebase. So we solved it once.
One agent after another
Every new agent meant hand-rolled tools, guardrails, evals and tracing — weeks per change.
Solve it once
Orchestration, RAG, tools, evals, memory and observability live once in the engine. Agents differ only by configuration.
Illustrative console — sample scores, real mechanism.
Release agents like software
An AI agent that changes silently is a liability. In the WAJ AI Engine, an agent changes the way software changes — through versions, gates, and approvals.
Numbered, immutable versions
Every promotion compiles the agent — prompts, model, tools, knowledge bindings — into a sealed snapshot. Production only ever runs sealed versions.
Eval-gated promotion
Golden test suites score every candidate. Below the pass threshold, promotion is blocked; overrides require explicit authority and are logged.
One-click rollback
Any prior version can be restored instantly. A bad release is a moment, not an incident.
A complete audit trail
Who changed what, when, what the eval score was, and which version answered any given conversation.
Your compliance team can inspect exactly which prompt, model, and tools were live at any moment — and who approved them.
Every conversation enters the same runtime.
One generic Runtime Agent, driven by the active profile. Channels differ — the path is the same.
Gemini, Claude, and leading open models from one catalog — no API keys to manage, no per-vendor contracts. Swap the model behind any agent and A/B it, no deploy.
See every conversation, all the way down.
It captures what agents do, keeps their knowledge fresh, and schedules the work that keeps quality up. Every trace is also an audit record — reconstruct any conversation, any version, any decision.
Full-depth tracing
Knowledge base management
Sources are managed per agent: ingest documents, search bilingually, refresh on a schedule.
Job orchestrator
Recurring work runs as tracked jobs — evals on prod data, KB updates, bulk translation, campaigns.
Memory inspector
Inspect short-term memory for a single session, or long-term memory built up across a user's history — per agent, per user.
The control plane your whole team runs — not just engineers
Product owners edit prompts, attach tools, run evals, and promote to production from one portal. Engineers built the engine once; nobody files a ticket to change an agent.
- ✓Edit a profile in the portal — the next request reflects it, no deploy.
- ✓Run pytest and eval suites from the portal, with per-test breakdowns.
- ✓Browse and replay sessions with tool calls, metadata and sparklines.
- ✓Promote per environment from /admin/env — behind the eval gate.
- ✓Edit → evaluate → promote, without a deploy or a code change
- ✓Every change versioned, every action audited
- ✓Staging and production separated — production only ever runs promoted, immutable versions
Sample data — the real portal is server-rendered and superuser-gated.
Your brand, every client
One engine, many branded deployments. Serve each of your clients — or each of your business units — from an isolated, scoped, independently released deployment.
Client-gated deployments
A deployment serves only the agents scoped to it. Everything else simply does not exist on that surface.
Their brand, their knowledge
Each deployment carries its own branding, knowledge bases, tools, and guardrails — assembled from configuration, not forked code.
Independent release trains
Each client’s agents are versioned, evaluated, and promoted on their own schedule. One client’s change never touches another’s production.
In-region by design
Deploy where your clients’ data must live. The engine runs in your cloud, in your region.
Built for platforms, resellers, and integrators who ship AI to their own customers.
Any agent your teams dream up.
The same loop — configure, evaluate, gate, trace — ships internal copilots and customer-facing agents alike. A sample of what fits.
Bug-response coding agent
Picks up bug reports and error alerts, reproduces the issue and drafts the fix as a pull request for human review.
Business analyst
“Why did revenue dip in March?” — answered with SQL over the warehouse, under the asker’s permissions.
How-to & policy assistant
Answers procedure and policy questions from internal docs, with the source cited every time.
HR helpdesk
Resolves leave, payroll and onboarding tickets; hands sensitive cases to a human with full context.
Risk assessment
Screens cases against policy checklists and drafts a scored assessment for sign-off.
Report automation
Compiles the weekly ops report from live data and delivers it on schedule.
Support agent
Deflects routine questions across chat and voice; escalates to humans with full transcript and sentiment.
Outbound campaigns
Runs retention and collections calls with approved scripts — consent and guardrails baked in.
Renewal reminders
Chases license, contract and subscription renewals before they lapse — polite, persistent, logged.
Customer data assistant
“How did my store do this week?” — customers query their own numbers, inline charts included.
Booking concierge
Answers services, prices and availability on each tenant’s site, in that tenant’s voice.
Lead qualification
Greets inbound leads, qualifies them and books the meeting with your team.
Branded agents for your clients
Run a separately scoped, separately promoted agent deployment for each client — their brand, their knowledge, their guardrails, one engine underneath.
See a governed agent ship — live
In one walkthrough: build an agent from configuration, run its eval suite, promote an immutable version to production, and roll it back. On your use case, in your language.
Config, not code · eval-gated promotion · full traces & cost analytics · vendor, model & cloud agnostic