Agentic Workflows

Production multi-agent AI orchestration system — architecture, pipeline patterns, and automation blueprints for coordinating autonomous AI agents, MCP tool integration, and cron-driven workflows.

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Overview

This repository documents a production-grade multi-agent orchestration architecture — a system where AI agents with specialized roles collaborate autonomously to execute complex, multi-step workflows.

Instead of a single monolithic agent, this architecture employs a hierarchical agent system:

• Orchestrator Agent — strategy, planning, and delegation
• Specialist Agents — focused execution (scraping, auditing, writing, coding)
• Tool Layer — MCP servers, browser automation, API integrations
• Scheduler Layer — cron-driven triggers with timezone-aware dispatch

The system processes data through automated pipelines — from extraction and validation to transformation and delivery — with quality gates at every stage.

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Architecture

┌─────────────────────────────────────────────────┐
│                 Orchestrator Agent               │
│         (Planning, Delegation, Strategy)          │
└──────────────────────┬──────────────────────────┘
          ┌────────────┼────────────┐
          ▼            ▼            ▼
┌─────────────────┐ ┌──────────┐ ┌──────────────────┐
│  Research Agent  │ │ Audit   │ │  Builder Agent    │
│  (Web, Search)  │ │ Agent   │ │  (Code, Execute)  │
└────────┬────────┘ │(Verify) │ └────────┬─────────┘
         │          └────┬─────┘          │
         ▼               ▼                ▼
┌─────────────────────────────────────────────────┐
│                  Tool Layer                       │
│  ┌──────────┐ ┌──────────┐ ┌──────────────────┐  │
│  │ Web/MCP  │ │ Browser  │ │  Terminal/Code   │  │
│  │ Tools    │ │Automation│ │  Execution       │  │
│  └──────────┘ └──────────┘ └──────────────────┘  │
└─────────────────────────────────────────────────┘

Agent Roles

Agent	Role	Capabilities
Orchestrator	Strategy & Delegation	Task decomposition, agent routing, quality control, session management
Scraper	Data Collection	Web extraction, MCP browser tools, API integration, structured parsing
Auditor	Quality Assurance	HTTP validation, content verification, deduplication, schema enforcement
Writer	Content Generation	Multi-channel formatting, tone control, platform-specific adaptation
Builder	Implementation	Code generation, debugging, testing, deployment

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Pipeline Patterns

Pattern 1: Extract → Validate → Transform → Deliver

[Source] → Extract → [Raw Data] → Validate → [Clean Data] → Transform → [Structured] → Deliver → [Target]
              │                        │                         │                       │
         Web Scraper              HTTP 200 Check            Enrichment              API Dispatch
         API Client               Content Verify            Normalization           File Export
         Browser Auto             Schema Check              Deduplication           Message Send

This is the core pattern used across all automated workflows. Every stage has quality gates that prevent bad data from flowing downstream.

Pattern 2: Agent Delegation Chain

User Request
    │
    ▼
Orchestrator ──→ Task Analysis ──→ Decomposition
    │                                      │
    ├── Research Agent ──→ Gather Context ──┘
    ├── Builder Agent ───→ Implement
    └── Audit Agent ─────→ Verify
                              │
                         Result → Feedback → Iterate

Pattern 3: Scheduled Automation

Cron Trigger (timezone-aware)
    │
    ▼
Pipeline Execution
    │
    ├── Script runs → stdout captured
    ├── Agent processes output
    ├── Decision: deliver or stay silent
    └── Delivery (chat, channel, file)

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MCP Tool Integration

The system leverages Model Context Protocol (MCP) for extending agent capabilities:

Tool	Purpose
Web Search	Real-time information retrieval, research
Web Extraction	Structured content parsing from URLs
Browser Tools	JavaScript-rendered page interaction, screenshots
Terminal	Code execution, git operations, system commands
File System	Read/write files, project inspection
Vision	Image analysis, screenshot understanding
Sequential Thinking	Complex multi-step reasoning
Image Generation	Creative content production

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Skills & Procedural Memory

The system uses a skill/procedural memory architecture where reusable knowledge is stored as structured SKILL.md files with:

• Frontmatter metadata — trigger conditions, category, description
• Numbered procedures — exact step sequences for recurring tasks
• Pitfalls section — known issues and their resolutions
• Verification steps — how to confirm success

Skills are loaded on-demand when relevant tasks are detected, keeping the agent's active context focused and efficient.

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Cron-Driven Architecture

Automated workflows run on schedules with:

• Timezone-aware scheduling (IST/UTC conversion)
• Chainable jobs — one job's output feeds another's context
• Silent execution — no notification when there's nothing new to report
• Rate-limited dispatch — per-channel limits, business hours enforcement
• Deduplication — across runs to prevent redundant processing

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Key Design Principles

1. Quality gates at every stage — validate before transform, verify before dispatch
2. Separation of concerns — each agent has a focused, non-overlapping role
3. Graceful degradation — one agent failure doesn't cascade to the whole pipeline
4. Observability — every stage produces structured output for debugging
5. Minimal context footprint — tools and skills loaded on demand, not persistently
6. Safety first — approval gates on destructive actions, no auto-publish without review

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Getting Started

This repo documents the architecture patterns. To implement a similar system, you'll need:

1. An AI agent framework with tool-calling and sub-agent delegation
2. MCP-compatible tools for web, browser, and terminal access
3. A scheduler for cron-driven automation
4. Persistent memory for cross-session state

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License

MIT