Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection

Designing effective agentic systems requires the seamless composition and integration of agents, tools, and models within dynamic and uncertain environments.
Most existing methods rely on static, semantic retrieval approaches for tool or
agent discovery. However, effective reuse and composition of existing components
remain challenging due to incomplete capability descriptions and the limitations
of retrieval methods. Component selection suffers because the decisions are not
based on capability, cost, and real-time utility. To address these challenges, we
introduce a structured, automated framework for agentic system composition that
is inspired by the knapsack problem. Our framework enables a composer agent to
systematically identify, select, and assemble an optimal set of agentic components
by jointly considering performance, budget constraints, and compatibility. By dynamically testing candidate components and modeling their utility in real-time, our
approach streamlines the assembly of agentic systems and facilitates scalable reuse
of resources. Empirical evaluation with Claude 3.5 Sonnet across five benchmarking datasets shows that our online-knapsack-based composer consistently lies on
the Pareto frontier, achieving higher success rates at significantly lower component
costs compared to our baselines. In the single-agent setup, the online knapsack
composer shows a success rate improvement of up to 31.6% in comparison to the
retrieval baselines. In multi-agent systems, the online knapsack composer increases
success rate from 37% to 87% when agents are selected from an agent inventory of
100+ agents. The substantial performance gap confirms the robust adaptability of
our method across diverse domains and budget constraints.