Building Your Own Framework

Master designing and building custom agentic AI frameworks from scratch

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Review the essential concepts for building custom agentic AI frameworks. Check off each item as you master it.

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Custom frameworks give complete control over architecture

Design exactly what you need without framework constraints or opinions

Best for specialized use cases and performance-critical applications

When general frameworks are too slow or don't fit your domain

Core agent loop: observe → reason → act → update

The fundamental cycle that drives all agent behavior

Agent class manages tools, memory, and execution flow

Central orchestrator that coordinates all agent components

Tool registry enables dynamic tool registration and execution

Memory system: short-term for context, long-term for facts

Balance recent conversation history with persistent knowledge

Memory consolidation prevents context window overflow

Summarize old messages to keep within token limits

Max iterations limit prevents infinite loops

Stop agents that can't complete their task after N attempts

State management tracks current goal and completed steps

Know exactly where the agent is in its execution

Error handling recovers gracefully from tool failures

Try/catch blocks and fallback strategies keep agents running

Schema validation with Pydantic ensures type safety

Validate tool inputs before execution to catch errors early

Structured logging essential for production debugging

JSON logs with trace IDs make troubleshooting possible

Rate limiting and cost controls protect against overruns

Token budgets and request limits prevent surprise bills

Composable design: build small, reusable components

Agent, memory, and tools should be independent modules

Test components in isolation before integration

Unit test tools and memory separately from the full agent