Inspiration
Most AI systems execute tasks based on predefined workflows. However, real autonomous systems should improve how they organize, delegate, and execute tasks over time.
We were inspired by the idea that intelligence is not only about generating good outputs — it is about improving decision-making strategies.
This project explores how a multi-agent system can evaluate its own workflow efficiency and adapt its task delegation strategy iteratively within a constrained hackathon environment.
What It Does
Adaptive Workflow Optimization Agent is a multi-agent system that:
Breaks a complex task into structured subtasks
Delegates execution to specialized worker agents
Evaluates workflow efficiency and output quality
Adjusts delegation rules based on feedback
Stores improved task strategies for future runs
The system improves how tasks are organized and executed over time.
How We Built It
The system consists of five components:
Planner Agent – Decomposes user input into subtasks
Worker Agents – Execute assigned subtasks
Evaluator Agent – Scores output quality and workflow efficiency
Strategy Agent – Updates delegation rules based on evaluation
Memory Layer – Stores optimized task graphs
The self-improvement loop works as follows:
Task → Planning → Delegation → Execution
Evaluate quality and efficiency
Assign score 𝑆 ∈ [ 0 , 10 ] S∈[0,10]
If 𝑆 < 𝑇 S<T, update delegation strategy
Store improved workflow configuration
Formally:
Task → Plan → Execute → Evaluate → Optimize Task→Plan→Execute→Evaluate→Optimize
Over multiple iterations, the system reduces redundancy and improves structural efficiency.
What We Learned
Multi-agent coordination requires structured communication
Evaluation-driven optimization improves task sequencing
Simple feedback rules can simulate adaptive intelligence
Clear agent roles improve architectural clarity
Challenges We Faced
Designing measurable workflow efficiency metrics
Preventing redundant agent communication
Keeping the system lightweight within time constraints
Demonstrating visible improvement in limited iterations
We focused on measurable optimization rather than feature complexity.
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