Multi-agent systems · 2025
Agentic Automation Orchestrator
LLM-based Planner, Researcher and Executor agents that coordinate to research, plan and carry out multi-step tasks with real tools.
−32%
tool calls per task
An agentic system in which LLM-based agents work together on multi-step tasks (research → plan → execute) using tools for web search, SQL querying and email or task creation.
Orchestration
A lightweight orchestration layer in Python and FastAPI handles tool routing, retries and context passing between the Planner, Researcher and Executor agents, using JSON-based action schemas.
Memory
A vector store of past tasks lets agents reuse earlier plans. In offline evaluation this reduced the average number of tool calls per successful task by 32%.
Measurement and guardrails
Logging and evaluation hooks track task success rate, average steps and tool error rate. Budget guardrails cap token and tool usage, and safety filters screen model outputs.
Next project
High-Throughput LLM Serving →
3.1×
token throughput