Description
Build useful AI agents with grounded context, controlled tools, and measurable outcomes.
Objective
Design intelligent agents that are reliable, safe, and measurable in real-world tasks.
Solution
Grounded Context
- 1Connect agent to domain-specific knowledge bases.
- 2Implement Retrieval Augmented Generation (RAG) with vector stores.
- 3Ensure context windows retrieve only relevant chunks.
Controlled Tools
- 1Define a registry of approved tools.
- 2Restrict execution policies to safe actions.
- 3Wrap business logic as custom tools.
Multi-Step Reasoning
- 1Use ReAct framework for reasoning + acting.
- 2Break tasks into smaller steps with intermediate outputs.
- 3Add error handling and fallbacks.
Memory
- 1Use ConversationBufferMemory for short-term context.
- 2Persist long-term memory for user preferences.
- 3Control recall to surface only relevant memory.
Measurable Outcomes
- 1Define KPIs: accuracy, latency, satisfaction, completion rate.
- 2Build evaluation harness with test queries.
- 3Log tool usage, reasoning steps, and outputs.
Deployment
- 1Package agent with Docker.
- 2Deploy on Kubernetes for scalability.
- 3Monitor with Prometheus/Grafana and ELK stack.
Tool Stack Used
- 🤖LangChain
- 🔧Vector Stores (FAISS, Pinecone)
- 🔧Custom Tools/APIs
- 🐳Docker
- ⎈Kubernetes
- 📊Prometheus
- 📊Grafana
- 🔧ELK Stack