The Hidden Complexity of Task Chains in AI Agents

When we first designed TaskWeaver, I had this delightfully naive belief: “Task chains are just sequences of steps. How hard could it be?”

Two years later, I laugh at my former self.

The Complexity Spectrum

Task chains in AI systems fall along a complexity spectrum:

Simple Linear Chain → DAG → Dynamic DAG → Reactive Execution → Multi-Agent Collaboration

And with each step to the right, the complexity increases exponentially.

Why Task Chains Break

In our experience, task chains fail for three primary reasons:

1. Context Collapse: Information gets lost between steps
2. Expectation Mismatch: What step A outputs isn’t what step B expects
3. Environmental Drift: The world changes during execution

Let me share a specific example from last week…

The Forgotten Parameter Problem

We had a task chain for analyzing GitHub issues:

def analyze_issues(repo_url):
    issues = fetch_issues(repo_url)
    analysis = categorize_issues(issues)  # This calls the LLM
    report = generate_report(analysis)    # This also calls the LLM
    return report

Simple enough. But users complained about inconsistent results.

After debugging, we realized the categorize_issues function sometimes returned different category schemas. When that happened, generate_report would fail because it expected specific categories.

The solution wasn’t adding more prompts or better instructions. It was structural:

def analyze_issues(repo_url):
    issues = fetch_issues(repo_url)
    # Define the schema first
    category_schema = define_category_schema(issues)
    # Then use the schema
    analysis = categorize_issues(issues, schema=category_schema)
    report = generate_report(analysis, schema=category_schema)
    return report

By making the schema explicit and passing it through the chain, we solved the problem. But this is just one example of dozens we’ve encountered.

The Mental Model That Helped

After months of struggling, we developed a mental model that helped: “Think of task chains as distributed systems, not sequential programs.”

This changed everything:

• We started passing explicit contracts between steps
• We added validation at each boundary
• We designed for partial failure and recovery
• We built observability into the chain itself

Our chains became longer and more verbose, but vastly more reliable.

Lessons for AI System Designers

If you’re building AI systems with task chains, consider these lessons:

1. Make implicit dependencies explicit
2. Design for observability from day one
3. Expect and handle failures at every step
4. Test with controlled degradation of inputs

Does this make your code less elegant? Yes.
Does it make your system more robust? Absolutely.

Sometimes I miss the days when I thought AI task chains were simple. But then I remember the hours spent debugging obscure failures, and I’m grateful for the complexity we’ve learned to navigate.