Build Production-Ready Agent Systems

Understand what agents really are and when to use them over traditional automation.

• What an agent is — beyond the buzzword
• Decision framework for agents vs automation vs workflows
• The anatomy of a production-grade agentic system

Design multi-step, multi-agent systems that coordinate reliably.

• DAG-based thinking for AI workflows
• Multi-agent coordination and delegation patterns
• Task decomposition and execution strategies

Connect agents to the systems that matter — APIs, databases, and internal tools.

• Designing reliable tool-use interfaces
• Error handling and retry patterns for external calls
• Security considerations for agent-to-system communication

Build retrieval pipelines that ground agent responses in real data.

• Designing retrieval pipelines for accuracy
• Structuring knowledge bases for agent consumption
• Avoiding hallucinations through grounded architectures

Choose the right model for each task and optimize cost-performance trade-offs.

• Model selection criteria for different task types
• Multi-model orchestration and routing strategies
• Cost-performance optimization patterns

Monitor, evaluate, and debug agent behavior in production environments.

• Agent behavior monitoring and logging
• Evaluation frameworks for output quality
• Debugging strategies and failure recovery

Build efficient systems that scale without burning through your budget.

• Reducing unnecessary token usage at scale
• Efficient architecture patterns for high-throughput systems
• On-prem vs API cost comparison models