Systematic AI Agent Building with Open-Source LLMs
A hands-on blueprint to design reliable agents using Llama, Mistral, and structured tool execution.
Core Architecture
- Planner: decomposes objective into actionable steps
- Executor: runs typed tools/APIs and validates outputs
- Memory: vector + key/value stores for short/long-term
- Supervisor: evaluates outcomes, retries, escalates
Engineering Practices
- Schema-first tools with strict validation
- Deterministic retries and bounded loops
- Prompt versioning and change tracking
- Telemetry: traces, metrics, and structured logs
Open-Source LLM Choices
- Llama: strong general performance and tooling
- Mistral/Mixtral: efficient inference, good reasoning
- Qwen: multilingual strengths
- Local hosting: vLLM, TGI, Ollama
Evaluation & Safety
- Task success rate and quality scoring
- Tool error analysis and recovery
- Safety policies and red-teaming
- Cost and latency budgets
Deployment Checklist
- Secure credentials and sandboxed tools
- Rate limits per agent and tenant
- Observability integration (OpenTelemetry)
- Rollout strategy and canary testing
Need help productionizing agents?
We help teams ship reliable agent systems with strong evaluation and safety.