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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.

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