Build Production Agentic AI
in Days, Not Months
Combine Cerebe cognitive services with the Sage agentic framework to ship Level 3+ autonomous AI products with persistent memory, multi-agent reasoning, and full observability — engineered through Dark Factory, our autonomous AI-native SDLC.
What ships you to production
Cognitive backend, agentic scaffold,
autonomous SDLC.
Cerebe is a managed cognitive services backend you can use behind any product. Sage scaffolds a production-ready agentic app on top. Dark Factory is the AI-native SDLC that lets agents do the heavy implementation while humans set direction and tweak the plan.
The SDLC
Dark Factory
The autonomous AI-native software development lifecycle. Agents author, critic, validate, and ship with verifiable safety gates from PRD all the way to production.
- PRD → cycle spec → merged code → soaked → promoted — agents drive each stage
- Evidence-required local critic (TDD classifier + verification routes)
- Multi-agent CI gates with deterministic enforcement
- Cycle-doc traceability gate — every code PR ties back to an approved plan
- Auto-merge on green; auto-deploy to preview; auto-promote on soak pass
- Cross-model adversarial review (Cursor, Codex, Copilot, Claude)
The Scaffold
Sage
The agentic application framework. Generates a complete FastAPI + Next.js + Helm project wired to Cerebe and Dark Factory — production-grade from the first commit.
- FastAPI backend with LangGraph agentic workflows
- Next.js 14 frontend with conversation management UI
- Helm charts, ArgoCD, CI/CD pipelines pre-configured
- Clerk auth, OpenTelemetry observability, guardrails built-in
- Contextual AI sidebar on every page
- Opinionated but fully ejectable — you own the code
The Engine
Cerebe
The cognitive services platform. Persistent memory, LLM routing, prompt management, multi-agent coordination, and meta-learning — through one API.
- Cross-session memory: working, episodic, long-term
- Model-agnostic LLM gateway with capability routing
- Version-controlled prompts with A/B evaluation
- Multi-agent deliberation with shared scratchpad
- Meta-learning loop: Plan → Learn → Reflect → Evolve
- Privacy-first: VPC, on-prem, data residency controls
Inside Dark Factory
From PRD to production,
engineered by agents.
Humans set direction: write the PRD, refine the cycle spec, prompt the agents, watch the dashboards. Agents do the engineering work: decompose the PRD into cycle specs, write code in TDD, run their own critics, validate in CI with adversarial multi-model review, deploy to preview, soak under load, and promote to production once every gate is green.
What makes this safe: every gate is deterministic and auditable. The critic refuses APPROVED without test + verification evidence. CI re-runs the same deterministic invariants on the pushed SHA — a critic-bypassed PR still hits enforcement at the GitHub layer. The cycle-doc gate refuses to merge code that lacks a traceable plan. Auto-merge fires only when every required check is fixed-and-blocking.
PRD
human-ledWhat to build, why, success criteria.
Cycle Spec
agentPlan agent decomposes PRD into cycle docs; critic reviews plan PR.
Local Dev
creator + criticCreator writes TDD; local critic enforces evidence per commit.
CI Gates
multi-agentDeterministic invariants + adversarial cross-model critics.
Preview
auto-deployAuto-deployed environment per PR. Smoke + E2E.
Soak
eval + obsTime-bounded. Eval harness, traces, error rates.
Production
auto-promoteCanary → full rollout. Auto-rollback on regression.
PRD — the human's job
A short product brief: the goal, the constraint, the acceptance criteria. Could be a Notion doc, a markdown file, or a GitHub issue. Humans set direction; agents take it from here (with humans tweaking and prompting along the way).
Cycle Spec — agent decomposes
A planning agent reads the PRD, scans the codebase, and drafts a cycle plan: scope, sub-cycles, file targets, validation gates, manifesto alignment, risks. The plan ships as a PR. Critic reviews. Once merged, it's the plan-of-record every implementation PR must trace back to.
Local Dev — creator + critic loop
The creator agent implements per the cycle plan, writing tests with source. After every commit, the local Cursor critic reviews against the AI-native manifesto: deterministic TDD classifier (production-glob change requires matching test-glob change or audited justification), verification-route activation (SDK/API contract tests for backend; Playwright for UI), evidence-or-justification per finding. Pre-push gate blocks bad commits before they reach GitHub.
CI Gates — the canonical layer
Multiple agents run in GitHub Actions. Critic-equivalents workflow re-runs the deterministic TDD classifier and verification-route checks against the pushed SHA — a critic-bypassed PR still gets enforcement at the GitHub boundary. The cycle-doc gate validates traceability: code PRs without an approved Cycle: N trailer fail. Cross-model critics (Cursor, Codex, Copilot) provide adversarial review. Auto-merge fires only when every required check is fixed-and-blocking.
Preview — auto-deploy per PR
The merge triggers a preview environment build — full stack, isolated namespace, Helm + ArgoCD. Smoke tests confirm health endpoints. UI E2E suites run against the deployed surface. Observability comes online: traces, metrics, logs all instrumented from scaffold.
Soak — validation under real conditions
Time-bounded soak in preview. The eval harness runs against real (or shadow) traffic. Observability watches error rates, latency tails, dependency health. Optional canary cohort exercises the change end-to-end. A regression at any layer halts promotion automatically.
Production — auto-promote with rollback
Soak passes → canary rollout → full deploy. Continuous observability watches the production surface; automated rollback fires on regression. The cycle doc auto-flips to complete via a follow-up docs PR (its own gate validates that all related PRs are merged and the implementation section is complete). Audit trail is closed.
Where humans stay in the loop
The PRD. Refining the cycle spec when the agent's first pass needs adjusting. Prompting the agents toward the right approach. Architectural review on novel cycle plans. Escalations when a deterministic gate flags something the agent can't justify. The heavy implementation, testing, deployment, and validation work runs on agents — with safety gates that make their work auditable.
The Scaffold
Sage — agentic apps from zero to deployed.
A complete production-grade agentic application in one scaffold step. FastAPI + Next.js + Helm, wired to Cerebe for cognition and Dark Factory for the SDLC. Opinionated where it matters; ejectable everywhere it doesn't.
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FastAPI + LangGraph backendAgentic workflows, ReAct loops, specialist delegation, bounded iteration. Multi-step reasoning out of the box.
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Next.js 14 frontendConversation history, sidebar, contextual AI on every page. Polished, keyboard-navigable, responsive.
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Production infrastructureHelm charts, ArgoCD GitOps, k3d local dev, GKE/EKS/AKS production. CI/CD pipelines pre-configured.
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Enterprise auth + observabilityClerk SSO/SAML, role-based access, OpenTelemetry traces, Prometheus metrics, Grafana dashboards, error tracking.
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You own the codeStandard FastAPI + Next.js + Helm. Modify anything. Eject completely. No runtime lock-in.
The Engine
Cerebe — cognitive services through one API.
The cognitive infrastructure that production agentic AI demands — persistent memory, LLM routing, prompt management, multi-agent coordination, evaluation. Stop building plumbing; ship product.
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Persistent memory fabricWorking, episodic, and long-term stores backed by Qdrant + Neo4j. Cross-session, multi-tenant, with COPPA-grade entity lifecycle controls.
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Capability-based LLM routingRoute by capability (reasoning-fast, reasoning-deep, vision, code), not model name. Switch providers without touching application code.
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Versioned prompts + meta-learningYAML-managed prompts with A/B evaluation. The PLRE loop (Plan → Learn → Reflect → Evolve) lets your agents improve continuously.
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Privacy-first deploymentCloud-managed, VPC-deployed, or fully on-prem. Doppler-managed secrets, External Secrets Operator, data residency controls.
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Python + TypeScript SDKsAuto-generated from OpenAPI. Contract-tested. Stable across language boundaries.
Why Momentiq
The best of every approach,
none of the trade-offs.
vs. Manual SDLC
Automated review with deterministic gates
Even teams shipping with Cursor and Copilot still review every PR by hand. Dark Factory automates the review with deterministic gates — the critic refuses APPROVED without evidence, and CI re-enforces the same invariants on the pushed SHA. Humans direct and tweak; agents do the heavy implementation.
vs. AI Frameworks
Production from day one
LangChain, CrewAI, AutoGen give you building blocks. We give you a deployable product on day one: auth, observability, conversation history, polished UI, infra — already built and wired to Cerebe.
vs. No-Code Builders
Real code you own
Lovable and Replit generate prototypes locked in their runtime. Sage generates standard FastAPI + Next.js + Helm. You modify it, deploy it anywhere, and own every line.
vs. Enterprise Platforms
Ship in days, not quarters
Salesforce Agentforce and Microsoft Copilot Studio lock you into their ecosystems. We're cloud-agnostic, on-prem deployable, and you keep your data, your models, and your engineering velocity.
Get Started
Build at AI-native velocity.
Join the early access program. We work directly with teams to scaffold their first agentic product with Sage, wire it to Cerebe, and stand up the Dark Factory pipeline that ships it.
Careers
Build the AI-native
engineering stack.
We're a small, senior team building the platform that defines how production AI gets engineered. Remote-first. High autonomy. Real impact.
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