How We Build Effective Agents

Ever wondered why some AI agents feel like they're playing 4D chess while others can barely play tic-tac-toe? The secret isn't in building more complex systems - it's actually about building simpler, more focused ones. Let's dive into how we build agents that actually work.

Start Simple: The Power of Tasks

Here's a truth that might surprise you: The most effective AI agents often start with no AI at all. Instead, they begin with clearly defined tasks and workflows. Think of it like teaching a child - you don't start with calculus, you start with counting.

A task in ControlFlow is beautifully simple:

sentiment_task = cf.Task(
    "Analyze the sentiment of the given text",
    context={"text": text},
    result_type=float
)

This isn't just a function call - it's a contract. It defines exactly what we want (sentiment analysis), what we're giving (text), and what we expect back (a float). This clarity is crucial for building reliable AI systems.

The Agent Team: Why One Size Doesn't Fit All

Remember trying to find that one "perfect" employee who could do everything? It usually doesn't work out. The same applies to AI agents. Instead of building one massive agent that tries to do everything, we build teams of specialized agents:

1. The Analyst: Focuses on understanding and breaking down problems
2. The Expert: Brings deep domain knowledge to specific tasks
3. The Reviewer: Evaluates outputs and suggests improvements

Each agent has a clear role and limited scope. This isn't just good architecture - it's good psychology. Even AI works better when it has clear boundaries and responsibilities.

Flows: The Choreography of AI

Think of flows as the choreography that brings tasks and agents together. But unlike a rigid dance routine, flows in ControlFlow are adaptable:

@cf.flow
def analysis_flow(text: str):
    # First, understand the context
    analysis = analyst_agent.analyze(text)
    
    # Then, bring in the expert for specific insights
    insights = expert_agent.examine(analysis)
    
    # Finally, have the reviewer check everything
    return reviewer_agent.validate(insights)

This isn't just a sequence of steps - it's a conversation between specialized agents, each contributing their expertise at the right moment.

The Art of Limiting Inputs

Here's a counterintuitive truth: The best agents often have the most limited inputs. Why? Because limitations create focus. When you tell an agent "do anything you want with this data", you're setting yourself up for chaos. Instead, we provide:

1. Specific Context: Only what's needed for the current task
2. Clear Constraints: Defined boundaries for actions
3. Expected Outputs: Precise formats for results

It's like the difference between asking "What should I do with my life?" and "Should I take this job offer?" One leads to philosophical meandering, the other to actionable insights.

Small Tasks vs. Big Prompts

Remember our discussion about probability in prompting? The same principle applies to agent tasks. Compare these approaches:

Bad approach:

huge_task = cf.Task(
    """Analyze this text, find all the insights, 
    suggest improvements, check for errors, and 
    write a comprehensive report..."""
)

Better approach:

analysis_task = cf.Task("Identify the main topics in the text")
insight_task = cf.Task("Find key insights for each topic")
review_task = cf.Task("Validate insights against source material")

The first approach is like trying to eat an entire cake in one bite. The second is like taking measured bites - more manageable, more reliable, and surprisingly, often faster.

Evaluating Success: The Feedback Loop

How do you know if your agents are actually effective? Through careful evaluation at each step:

1. Task-Level Metrics: Each task should have clear success criteria
2. Flow-Level Validation: The overall process should be measurable
3. Agent-Specific KPIs: Each agent's performance should be trackable

But here's the key: Don't just measure the final output. Measure the process. An agent that gets the right answer through a wrong process is a ticking time bomb.

The Path Forward

Building effective agents isn't about creating the most sophisticated AI - it's about creating the most reliable one. Start with clear tasks, build specialized agent teams, choreograph them with flows, and always, always measure what matters.

Remember: The goal isn't to build agents that can do everything. It's to build agents that can do the right things, reliably and repeatedly.

Next Steps

• Learn about Tasks in detail
• Explore Flows for complex operations
• Understand Agent Teams and their roles