A practitioner's reference for AI agent architecture and engineering patterns.
How agents use code execution to filter retrieved web content before it enters the context window, improving accuracy and reducing token costs.
How agents can execute tool calls inside a sandboxed code environment to reduce round-trip latency and token overhead in multi-step workflows.
How coding agents automate the entire LLM fine-tuning workflow from GPU selection to model deployment using natural language instructions.
Beyond simple retrieve-then-generate: intelligent agents that decide when, what, and how to retrieve, then critique and correct their own retrieval.
How AI agents improve over time without retraining: token-space learning from successful trajectories, Reflexion self-critique, and self-evolving architectures.
Patterns and frameworks for coordinating multiple specialized AI agents including supervisor, peer-to-peer, debate, and mixture of experts.
A filesystem-based approach to tool management that achieves 98% token savings by loading tool definitions on-demand rather than sending all tools on every request.
*LangChain's coding agent vaulted from outside the Top 30 to the Top 5 on Terminal Bench 2.0 by engineering the scaffolding, not the AI.*
How performance degrades within supported context limits, and practical strategies to detect, measure, and mitigate both failure modes.
The discipline of optimizing what enters the context window — a key skill for practitioners building reliable agents alongside prompt engineering.
Reduce inference costs by 90% and time-to-first-token by 80% by reusing computed attention states across requests with identical prefixes.
A look at Context-Bench, Letta's benchmark for measuring how well language models perform context engineering tasks including filesystem traversal and dynamic skill loading.
Measuring agent performance across component accuracy, task completion, trajectory quality, and system-level metrics with benchmarks and LLM-as-judge.
How agents maintain context, learn from past interactions, and build persistent knowledge across sessions using layered memory architectures.
Reasoning plus Acting — the foundational loop that enables AI agents to think through problems and take targeted action in the world.
The bridge between language models and real-world actions, enabling agents to query APIs, execute code, and interact with external systems.
Google's open protocol enabling AI agents to discover, communicate, and collaborate across organizational boundaries using standardized task exchange.
An official MCP extension enabling tools to return interactive UI components — dashboards, forms, and visualizations — that render directly in conversations.
An open standard from Anthropic that defines how AI agents connect to external tools, data sources, and services through a composable server architecture.
An open industry protocol enabling AI agents to shop across any participating merchant using unified APIs for checkout, identity linking, and order management.
Defense in depth for AI agents: input validation, output filtering, tool sandboxing, guardian agents, and OWASP LLM security risks.