Tool-Use Architectures for Autonomous Agents
Defining the Tool Interface
Every tool an agent can use must have a precise schema definition including input types, output types, error modes, and latency expectations. Ambiguous tool definitions lead to incorrect tool selection and cascading failures in multi-step plans.
Planning and Tool Selection
Modern tool-use agents decompose high-level goals into sub-tasks and match each sub-task to the most appropriate tool. The planning phase considers tool capabilities, estimated latency, and cost to produce an execution plan before any tool is invoked.
Error Recovery in Tool Chains
When a tool call fails mid-chain, the agent must decide whether to retry, substitute an alternative tool, or escalate to a human operator. Our agents maintain a rollback log so partial progress can be unwound cleanly if recovery is not possible.
ActiveMotion Team
AI Research
The ActiveMotion engineering and research team
Articles connexes
Building Reliable AI Agents for Enterprise Workflows
How to design autonomous agents that handle real-world complexity, recover from failures, and integrate with existing enterprise systems at scale.
IA agentique vs automatisation classique : pourquoi la distinction compte
Comprendre le spectre — de l'automatisation par règles aux copilotes jusqu'aux agents pleinement autonomes — et pourquoi les entreprises ont besoin d'une IA qui agit au lieu de se contenter de suggérer.
La révolution de la mémoire : comment les agents contextuels transforment les opérations
Des prompts sans état à la mémoire persistante — comment les agents disposant d'un contexte long terme délivrent des résultats métier que les systèmes LLM classiques ne peuvent atteindre.
Commentaires
Aucun commentaire pour le moment. Soyez le premier !