Production-grade agent framework with RAG, tool use, and multi-step reasoning
AI Agent Builder provides the complete toolkit for building, testing, and deploying enterprise AI agents. Orchestrate LLMs with retrieval, tool calling, and guardrails — from prototype to production in days, not months.
Built-in vector search with hybrid retrieval across structured and unstructured data. Automatic chunking, embedding, and reranking deliver precise context to every LLM call.
Define custom tools that agents can invoke — database queries, API calls, calculations, or any business logic. Sandboxed execution with full audit logging.
Agents decompose complex tasks into subtasks, reason over intermediate results, and self-correct. Configurable reasoning depth and fallback strategies prevent runaway execution.
Route requests to the optimal model based on task complexity, cost, and latency requirements. Support for OpenAI, Anthropic, open-source models, and custom fine-tunes.
Specify the agent's role, available tools, knowledge sources, and behavioral guardrails through a declarative configuration.
Attach RAG-indexed document collections, database connections, API endpoints, and custom functions as the agent's operational toolkit.
Run the agent against test scenarios in a sandbox environment. Inspect reasoning traces, tool calls, and outputs to refine behavior before deployment.
Push agents to production with built-in versioning, A/B testing, and real-time monitoring of accuracy, latency, cost, and user satisfaction.
Declarative agent definition and pre-built components cut development time from months to days.
Guardrails, output validation, and human-in-the-loop checkpoints ensure agents behave predictably in enterprise environments.
Integrated RAG ensures agents answer from your actual data — not hallucinated content — with source citations for every response.
Smart model routing and caching reduce LLM costs by directing simple tasks to smaller models and caching repeated retrievals.
AI Agent Builder is a layered orchestration framework. The agent runtime manages conversation state, reasoning loops, and tool dispatch. A shared memory layer persists context across sessions, while the retrieval tier provides real-time access to knowledge bases and enterprise data.
A capability knowledge base and RFQ parser let an Agent classify each requirement as compliant, non-compliant, or uncertain, cite evidence, and fill the original Excel template.
Industry scanning, target identification and post-investment tracking all ran on manual effort, with internal and external data siloed apart. The fund built an AI investment intelligence system on MOI: multi-source data is collected and structured automatically, linked into industry-chain, company and technology knowledge graphs, with reports and alerts generated on top.
Students don't know what to read or how; parents can't see progress; teachers can't follow every child. Tencent built AI Tutor on MOI, using a multi-agent architecture that turns one reading session into a personalised path, interactive Q&A, comprehension testing and knowledge expansion.
Go from idea to production AI agent with integrated RAG, tool use, and enterprise-grade guardrails — all on your data.