PRODUCTION AI · AGENTIC AUTOMATION · REVOPS ENGINEERING

Autonomous Revenue Ops

AI may propose. Software validates. Policy authorizes. Evidence proves.

A production-oriented reference implementation for revenue automation where AI contributes research, qualification and outreach judgment without owning authorization, durable state or uncontrolled side effects. The system combines multi-model agents, deterministic policy, restart-safe orchestration, SaaS adapters, release engineering and operational evidence.

Open Live Space ↗Inspect Source ↗90-sec Proof ↗

Autonomous Revenue Ops production AI automation architecture

The engineering problem

A happy-path lead-scoring demo is not autonomous operations.

An LLM can classify a lead and draft an email in seconds. The difficult engineering starts when the workflow receives duplicates, malformed input, low-confidence output, stale state, provider errors, approval cases, rate limits and actions with real business consequences. I built this project to make those boundaries explicit: the model proposes; deterministic software decides what is allowed; durable workflow state determines what can execute; and retained evidence supports each maturity claim.

End-to-end architecture

Separate reasoning, authorization, state and execution.

Lead / signed webhook / n8n
          ↓
Authentication + typed validation
          ↓
Correlation + DB-backed idempotency
          ↓
Durable workflow run
          ↓
Research Agent → Qualification Agent
          ↓
Structured-output validation
          ↓
Deterministic policy
   ↙          ↓           ↓           ↘
AUTO_ROUTE  HUMAN_REVIEW  RESEARCH_MORE  BLOCK
   ↓          ↓           ↓             ↓
READY       checkpoint   checkpoint    complete
   ↓          ↓           └──→ research
   └──────────┘
          ↓
Outreach Agent
          ↓
Execution claim / lease
          ↓
HubSpot · Salesforce · Slack · SMTP · Webhook
          ↓
Retry · Circuit Breaker · DLQ · Replay
          ↓
Execution receipt
          ↓
Measured runtime analytics + retained evidence

Control plane

The important work happens around the model.

AI judgment

Research, qualification and outreach agents produce structured proposals and evidence.

Authorization

Deterministic policy decides AUTO_ROUTE, HUMAN_REVIEW, RESEARCH_MORE, NURTURE or BLOCK.

Workflow state

Durable orchestration, revisions, idempotency, leases, checkpoints and execution receipts live outside the LLM.

Side effects

HubSpot, Salesforce, Slack, SMTP and webhook adapters execute only after policy and workflow authorization.

Reliability

Retries, circuit breaker, DLQ, replay and recovery paths make failure explicit rather than exceptional.

Security

API-key boundary, signed webhooks, replay protection, secret-safe evidence and hardened container/runtime controls.

Evaluation

Frozen deterministic cases, prompt manifests, release gates and retained external-validation contracts.

Operations

Measured runtime aggregates, Prometheus-compatible metrics and reviewer-facing dashboard surfaces.

Verified evidence

The portfolio claim is tied to inspectable gates.

EvidenceObservedResult

Regression suite119/119PASS

Policy benchmark6/6100%

Deterministic agent decisions7 cases100%

Public surface contracts3/3PASS

Policy violations0PASS

Release version contractv0.10.0PASS

Release-integrity verificationSBOM + manifest + provenancePASS

Hardened container livenessHTTP 200PASS

Hardened container readinessHTTP 200PASS

The 100% deterministic agent result is controlled contract/governance evidence, not a claim that OpenAI, Anthropic or Gemini have 100% real-world accuracy. Live provider and SaaS execution remains a separate retained-evidence step.

Public proof stack

Use the right surface for the proof being shown.

SurfacePurposeStatus

Hugging FaceInteractive behavior, dataset and system cardLive

Streamlit Operations CenterMeasured-runtime reviewer dashboardSource ready

n8nVisual orchestration and execution traceWorkflow ready

Render staging APIFastAPI/OpenAPI sandboxDeployment next

Grafana CloudAggregate operational telemetryDashboard ready

PostmanForkable API examplesCollection ready

GitHub Releases / GHCRVersioned runtime artifactWorkflow ready

Reviewer path

Portfolio case study → GitHub architecture → Hugging Face demo
→ Streamlit Operations Center → n8n workflow → Postman API
→ Grafana telemetry → CI / release evidence → retained live validation

Maturity boundary

Strong production-candidate evidence without overstating what has not been run.

The current system is release-integrity controlled, contract-tested, restart-safe, security-hardened and single-replica by design. It is not yet Production Validated. Real provider/SaaS/staging runs must be executed through the retained-evidence workflow, long-window SLO evidence must be collected, and the SQLite persistence boundary must move to a shared transactional backend before horizontal-replica claims are made.

Public artifacts

Source → demo → evaluation → system card → operational proof.

GitHub Source

Architecture, typed API, tests, CI, release controls, n8n, Postman, Grafana and deployment assets.

Open ↗

Hugging Face Space

Interactive policy and system demonstration.

Open ↗

Evaluation Dataset

Public regression/evaluation cases used to keep behavior inspectable.

Open ↗

System Card

Intended use, limitations and evidence boundaries.

Open ↗

Public Proof

Fast reviewer path through architecture, controls and verified evidence.

Open ↗

Engineering takeaway

Autonomy is useful only when the surrounding system knows when not to act.

This project is less about replacing revenue teams with an agent and more about engineering a trustworthy control plane: bounded AI judgment, explicit policy, durable state, recoverable execution, observable outcomes and evidence-linked releases.

Try the Space ↗Read the engineering article Agentic AI Academy