Agent Engineering

A comprehensive resource for understanding AI agent architectures, design patterns, and implementation strategies. From basic loops to advanced multi-agent systems.

Code Examples

Every pattern includes implementations in pseudo-code, Python (LangChain/LangGraph), and C# (Microsoft Agent Framework).

The Basic Agent Loop
function agentLoop(task):
    context = initializeContext(task)

    while not task.isComplete():
        observation = perceive(context)
        thought = reason(observation)
        action = decide(thought)
        result = execute(action)
        context = update(context, result)

    return context.result
def agent_loop(task: Task) -> Result:
    context = initialize_context(task)

    while not task.is_complete():
        observation = perceive(context)
        thought = reason(observation)
        action = decide(thought)
        result = execute(action)
        context = update(context, result)

    return context.result
public Result AgentLoop(Task task)
{
    var context = InitializeContext(task);

    while (!task.IsComplete())
    {
        var observation = Perceive(context);
        var thought = Reason(observation);
        var action = Decide(thought);
        var result = Execute(action);
        context = Update(context, result);
    }

    return context.Result;
}

Benchmarks

Industry-standard benchmarks for measuring agent capabilities.

SWE-bench
Software engineering task completion
WebArena
Web browsing and interaction tasks
GAIA
General AI assistant capabilities
View all benchmarks
SWE-bench Verified 72%
WebArena 42%
GAIA Level 1 75%

Top agent scores as of 2025

Resources

Curated papers, frameworks, and tools for building AI agents.

Browse Resources