Build an AI Agent (From Scratch)
AgentA book about building AI agents with tools, memory, planning, and multi-agent systems.
Capabilities10 decomposed
tool integration and invocation framework
Medium confidenceTeaches patterns for binding external tools (APIs, functions, services) to AI agents through structured schemas and invocation mechanisms. Covers tool discovery, parameter binding, error handling, and result parsing to enable agents to autonomously select and execute appropriate tools during task execution.
Provides systematic patterns for designing tool registries and invocation mechanisms that work across multiple LLM providers (OpenAI, Anthropic, etc.) rather than single-provider implementations, with emphasis on graceful degradation and error recovery
More comprehensive than provider-specific tool-calling docs because it abstracts patterns across LLM ecosystems and covers multi-agent tool coordination scenarios
agent memory management and context persistence
Medium confidenceDescribes strategies for maintaining agent state across multiple reasoning steps, including short-term working memory, long-term knowledge storage, and context window optimization. Covers memory architectures like sliding windows, summarization, vector embeddings for retrieval, and hybrid approaches to balance context relevance with token constraints.
Systematically covers memory trade-offs across agent lifecycle (working memory vs. long-term storage, retrieval latency vs. relevance) with patterns for hybrid approaches rather than single-strategy recommendations
More holistic than individual RAG or context-management tutorials because it positions memory as a core architectural decision affecting agent autonomy, cost, and reasoning quality
agent planning and reasoning decomposition
Medium confidenceTeaches methodologies for breaking complex tasks into sub-goals and reasoning steps, including chain-of-thought prompting, tree-of-thought search, and hierarchical planning. Covers how agents can decompose ambiguous user requests into concrete action sequences, evaluate alternative plans, and adapt when execution fails.
Covers planning as a spectrum from simple linear decomposition to tree-search and hierarchical approaches, with explicit guidance on when to use each pattern based on task complexity and computational budget
More comprehensive than single-pattern tutorials (e.g., just chain-of-thought) because it addresses planning as a core architectural choice affecting agent autonomy and reasoning quality
multi-agent coordination and communication
Medium confidenceDescribes patterns for orchestrating multiple specialized agents working toward shared goals, including message passing, role assignment, consensus mechanisms, and conflict resolution. Covers how agents can delegate tasks, share context, and coordinate execution without central control.
Treats multi-agent coordination as a first-class architectural pattern with explicit guidance on communication protocols, role hierarchies, and conflict resolution rather than treating it as an extension of single-agent design
More systematic than ad-hoc multi-agent examples because it covers coordination patterns (hierarchical, peer-to-peer, publish-subscribe) and their trade-offs
agent autonomy and decision-making loops
Medium confidenceTeaches the core agent loop architecture: perception (observing state), reasoning (deciding actions), and action (executing decisions). Covers how to implement feedback loops, handle execution results, and determine when agents should stop or escalate to humans. Includes patterns for balancing autonomy with safety constraints.
Frames the agent loop as a control system with explicit feedback mechanisms and safety constraints rather than a simple request-response pattern, emphasizing the role of observation and adaptation
More foundational than tool-calling or planning tutorials because it addresses the core loop that makes agents autonomous and provides patterns for safe, bounded autonomy
agent evaluation and testing frameworks
Medium confidenceDescribes methodologies for measuring agent performance, including task success metrics, reasoning quality assessment, and cost-efficiency analysis. Covers how to design test suites for agent behavior, handle non-deterministic outputs, and benchmark against baselines. Includes patterns for continuous evaluation and improvement.
Addresses evaluation as a core architectural concern rather than an afterthought, with patterns for handling non-deterministic outputs and continuous improvement cycles
More comprehensive than generic LLM evaluation because it addresses agent-specific challenges like multi-step reasoning quality and cost-per-task optimization
error handling and agent failure recovery
Medium confidenceTeaches patterns for detecting agent failures (execution errors, invalid outputs, timeout), implementing recovery strategies (retry with backoff, alternative tool selection, task decomposition), and graceful degradation. Covers how to distinguish recoverable errors from fundamental failures and when to escalate to humans.
Treats error recovery as a core agent capability with explicit patterns for classification, retry strategies, and escalation rather than generic exception handling
More agent-specific than generic error handling because it addresses multi-step reasoning failures and distinguishes between tool failures, reasoning errors, and LLM output issues
agent prompt engineering and instruction design
Medium confidenceDescribes techniques for crafting effective prompts that guide agent behavior, including role definition, task specification, constraint encoding, and output formatting. Covers how to structure instructions for multi-step reasoning, tool use, and error recovery. Includes patterns for prompt versioning and A/B testing.
Treats prompt engineering as a systematic discipline with patterns for role definition, constraint encoding, and output formatting rather than ad-hoc trial-and-error
More agent-focused than generic prompt engineering guides because it addresses multi-step reasoning, tool use, and error recovery in prompts
agent observability and execution tracing
Medium confidenceTeaches how to instrument agents for visibility into their reasoning process, including logging decision traces, capturing tool invocations, and recording intermediate results. Covers structured logging formats, trace visualization, and debugging techniques for understanding why agents made specific decisions or failed.
Frames observability as essential to agent development and debugging, with patterns for structured tracing of multi-step reasoning and tool invocations
More agent-specific than generic observability because it addresses tracing of reasoning steps, tool calls, and decision justifications
agent cost optimization and resource management
Medium confidenceDescribes strategies for reducing agent operational costs, including token optimization (context pruning, summarization), LLM model selection (balancing capability vs. cost), and caching strategies. Covers how to measure cost-per-task and identify optimization opportunities without sacrificing performance.
Addresses cost as a core architectural concern in agent design, with patterns for token optimization and model selection rather than treating it as an afterthought
More comprehensive than generic cost-reduction tips because it covers agent-specific optimizations like context pruning and multi-model selection strategies
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Interface between LLMs and your data
Best For
- ✓developers building autonomous agents with external service dependencies
- ✓teams designing tool-calling architectures for multi-step workflows
- ✓engineers implementing function-calling patterns across multiple LLM providers
- ✓developers building conversational agents with multi-turn interactions
- ✓teams implementing RAG (Retrieval-Augmented Generation) for agent knowledge
- ✓engineers optimizing token efficiency in long-running autonomous workflows
- ✓developers building agents for multi-step reasoning tasks (research, planning, debugging)
- ✓teams implementing hierarchical task decomposition for complex workflows
Known Limitations
- ⚠Book format limits hands-on implementation depth — requires supplementary code examples from GitHub repository
- ⚠Tool schema design patterns may not cover domain-specific edge cases without additional engineering
- ⚠No guidance on tool versioning, deprecation, or backward compatibility strategies
- ⚠Memory architecture choices involve trade-offs between latency, accuracy, and cost that vary by use case
- ⚠Book provides patterns but not production-ready implementations for all memory backends
- ⚠Summarization quality depends heavily on LLM capability and domain specificity
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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A book about building AI agents with tools, memory, planning, and multi-agent systems.
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