Inteligens Agents Framework

Agent-Native Engineering with Human Governance

A governance-first execution framework for multi-agent AI systems.

A lightweight orchestration layer designed to coordinate specialized AI agents
as a senior engineering team — while keeping humans firmly in control.

🚀 Overview

Inteligens Agents Framework is an Agent Operating Framework (AOF)
built around one core principle:

Autonomy without governance is chaos.

It enables:

🧠 multi-agent planning
🧭 structured execution flows
🛡️ explicit human-in-the-loop approval gates
⚡ IDE-agnostic operation (Cursor, VSCode, CLI, Antigravity)
🏗️ sprint-aware delivery

This is not a fully autonomous system by design.
It is a controlled execution environment.

🎯 Design Philosophy

The framework follows three core principles:

1. Governed AI

Autonomy without control is a liability.
Every execution step is observable and reviewable.

2. Agent Specialization

Each agent has a clear senior role (PO, Architect, Backend, QA…).

3. Incremental Delivery

Work is organized by phases and sprints to ensure real software delivery.

🏗️ Architecture

Flow:

User → Router → Swarm Planner → Execution Runner → Human Review

⚡ Quick Start

0. Set up product context (recommended)

Copy the product template to your project root and fill it in:

cp .agents/docs/PRODUCT_TEMPLATE.md PRODUCT.md

This gives every agent persistent context about your product — stack, architecture decisions, compliance constraints, conventions, and documentation language. Without it, agents start from zero on every sprint.

See docs/guides/PRODUCT_SETUP.md for full instructions.

1. Generate a plan

python .agents/swarm/swarm_planner.py \
  --task "build a RAG pipeline" \
  --sprint "Sprint 1"

The plan is saved to .agents/swarm/execution_plan.json by default.
If PRODUCT.md is present, product context is automatically injected into the plan.

2. Initialize execution

python .agents/swarm/execution_runner.py --init

Or specify a custom plan path:

python .agents/swarm/execution_runner.py --init path/to/plan.json

3. Execute next step

python .agents/swarm/execution_runner.py --next

4. Mark step as done

python .agents/swarm/execution_runner.py --done

🧠 Assisted Auto‑Execution

v1 introduces assisted auto‑execution, which provides:

step‑by‑step execution
sprint context awareness
agent‑specific prompts
execution journal
human approval loop

This allows high automation without losing control.

📂 Project Structure

.agents/
  agents/
  router/
  swarm/
docs/
  manifesto/
  roadmap/
  rfcs/
  guides/
examples/

🧪 Compatibility

The framework is IDE‑agnostic and works with:

Cursor
VSCode
Antigravity
Claude Code
Pure CLI

🛡️ Security Model

The framework intentionally:

❌ does NOT auto‑execute code
❌ does NOT mutate repositories silently
✅ requires human confirmation
✅ keeps full execution trace

Human-in-the-Loop Safety

The framework supports optional approval gates between critical steps.

See:

docs/architecture/APPROVAL_GATES.md

📚 Documentation

Getting Started

Usage Guide - How to operate the framework
Language-Specific Guide - Setup for Python, Node.js, Go, Rust, Java, C#
Scrum Guide - How the framework implements Scrum

Architecture & Design

Agents Framework - Framework overview
Approval Gates - Human checkpoints
RFC-001: Approval Gates - Technical specification
RFC-002: Step Dependencies - Dependency management (v1.1)
RFC-003: Advanced Dependency Management - Intelligent assistance (v1.2)
RFC-004: Ad-hoc Agent Sessions - Tracked direct agent calls (v1.1)
RFC-005: Production Readiness Sprint - MVP → Production transition (v1.1)
RFC-006: Bug Triage and Fix Workflow - Structured bug handling (v1.1)

Roadmap

Public Roadmap - High-level product direction
Evolution Roadmap - Technical evolution strategy

🗺️ Roadmap

✅ v1.0 — Assisted Auto-Execution (Current)

Agent Router (intent → specialist)
Swarm Planner (multi-agent plan generation)
Execution Runner (step-by-step assisted flow)
Sprint-aware execution context
Human-in-the-loop by design
IDE-agnostic operation
Approval Gates (human checkpoints between phases)

🟡 v1.1 — Governance Hardening (Planned)

Focus: production safety and execution discipline.

Improved execution observability
Sprint metrics enrichment
Stronger auditability of agent actions
Execution navigation (--back command)
Step dependency management (--block, --done --partial, --resolve)
Automatic dependency resolution detection
Ad-hoc review mode with full observability
Ad-hoc agent sessions (--session, --session-log, --session-close) for tracked direct agent calls
Production Readiness Sprint (--production-sprint) for structured MVP → Production transition
Bug Triage and Fix Workflow (--report-bug, --triage-bug, --bug-fix-sprint) for structured bug handling

🟡 v1.2 — Advanced Dependency Management (Planned)

Focus: intelligent dependency assistance and planning integration.

Automatic planning review triggers (PO + Scrum Master)
Intelligent workaround suggestions
Blocking metrics and analytics (--metrics)
Circular dependency detection
Step reordering suggestions (--suggest-reorder)
Backlog integration suggestions

🟡 v1.3 — Intelligent Planning (Planned)

Focus: smarter planning from real inputs.

Backlog ingestion (issues → execution plan)
Context-aware planning
Smarter task decomposition
Improved router confidence scoring
Adaptive project type detection (ML, Hardware, IoT, Data Science, Security, Edge, etc.)
Project-specific step templates (automatic step selection based on project type)
Hybrid project support (combines steps from multiple project types)

🔵 v2.0 — Adaptive Swarm (Future)

Focus: controlled autonomy at scale.

Semi-autonomous execution loops
Multi-sprint orchestration
Parallel swarm coordination
Dynamic plan adaptation

🧪 Research Track (Exploratory)

These are intentionally not committed to a release:

Daily Sync Orchestrator
Fully distributed agents
Self-healing execution loops
Cross-project swarm memory

📍 See full details in:

docs/roadmap/PUBLIC_ROADMAP.md
docs/roadmap/EVOLUTION_ROADMAP.md

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for:

Branch workflow and guidelines
Commit message conventions
Pull request process
Release process
Development setup

Quick start: Open an issue before large changes, then follow the branch workflow in CONTRIBUTING.md.

📜 License

MIT License.

Built with ⚙️ by Inteligens

Inteligens-ai/inteligens-agent-framework