AI Agents: Building an Autonomous Task Orchestration System

Background

This was one of our most technically ambitious projects: building a multi-agent orchestration platform that could take high-level goals and decompose them into coordinated, autonomous task chains executed by specialised AI agents.

The use case: a content and research business that needed to automate complex multi-step workflows. Tasks like “research competitor X, extract their top 10 content pieces, identify content gaps, draft a brief for each gap, and post to Notion” used to take a human 3 to 4 hours.

The Architecture Challenge

Single-agent AI systems are relatively straightforward. Multi-agent systems, where multiple specialised agents collaborate, hand off tasks, and share context, introduce a different class of problems:

Problems we needed to solve:

1. Task decomposition: How do you reliably break a high-level goal into executable sub-tasks?
2. Agent routing: How do you decide which specialised agent handles which sub-task?
3. Context passing: How do agents downstream receive the right context from agents upstream?
4. Failure recovery: What happens when one agent in a chain fails or produces bad output?
5. Observability: How does the operator know what’s happening inside a running agent chain?

The Solution

We built a central Orchestrator that sits above all specialised agents. It receives the high-level goal, uses an LLM call to produce a structured execution plan (JSON), and then dispatches tasks to the appropriate agents in the right sequence.

Agent Types Built

Orchestration Flow

Goal: "Research [topic], identify 5 content gaps, draft briefs for each"

Orchestrator → produces execution plan:
  Step 1: Research Agent (searches, extracts, returns structured data)
  Step 2: Analysis Agent (receives Step 1 output, identifies gaps)
  Step 3 (parallel): Writer Agent × 5 (one per gap, runs concurrently)
  Step 4: Formatter Agent (assembles all drafts)
  Step 5: Publisher Agent (posts to Notion)

Step 3 runs in parallel: all 5 writer agents execute simultaneously, reducing total time by 4× compared to sequential execution.

Failure Recovery

Each agent step has a retry policy with exponential backoff. If an agent fails after 3 retries, the orchestrator:

1. Logs the failure with full context
2. Attempts a fallback strategy (different model, simpler prompt)
3. If still failing, marks the step as degraded and continues with partial output
4. Alerts the operator via webhook

This means a single agent failure doesn’t bring down the entire chain.

Observability Dashboard

One of the non-obvious requirements: the humans overseeing this system needed to see what was happening in real time. We built a live dashboard showing:

• Active agent chains with step-by-step status
• Token usage per agent per run (cost tracking)
• Success/failure rates per agent type over time
• Average chain completion time by goal type

The Stack

• Orchestration: Custom Python service + LangGraph for complex agent graphs
• LLMs: Claude 3.5 Sonnet (orchestrator + writer) + Claude Haiku (research + formatting)
• Memory: Redis for short-term context passing between agents
• Queue: Celery + Redis for parallel agent dispatch
• Database: PostgreSQL for run history and analytics
• Dashboard: Next.js + Recharts
• Webhooks: Custom event bus for real-time status updates

Results

The platform now runs hundreds of concurrent task chains. The team it replaced wasn’t eliminated. They were redirected to the tasks that actually required human judgement: strategy, client relationships, and quality review.

Build time: 6 weeks from architecture design to production deployment.