{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-build-an-ai-agent-from-scratch","slug":"build-an-ai-agent-from-scratch","name":"Build an AI Agent (From Scratch)","type":"product","url":"https://www.manning.com/books/build-an-ai-agent-from-scratch","page_url":"https://unfragile.ai/build-an-ai-agent-from-scratch","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-build-an-ai-agent-from-scratch__cap_0","uri":"capability://tool.use.integration.tool.integration.and.invocation.framework","name":"tool integration and invocation framework","description":"Teaches patterns for binding external tools (APIs, functions, services) to AI agents through structured schemas and invocation mechanisms. 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Covers memory architectures like sliding windows, summarization, vector embeddings for retrieval, and hybrid approaches to balance context relevance with token constraints.","intents":["I need my agent to remember previous conversation context across multiple interactions","I want to implement efficient memory that doesn't blow up token usage on long-running tasks","I need to design a knowledge base that agents can query to augment reasoning"],"best_for":["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"],"limitations":["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"],"requires":["Understanding of token counting and context window limits for target LLM","Familiarity with vector databases or embedding services (optional for advanced patterns)","Storage infrastructure for persistent memory (database, vector store, or file system)"],"input_types":["conversation history and interaction logs","agent reasoning traces and decision records","domain knowledge documents and reference materials"],"output_types":["summarized context for next reasoning step","retrieved relevant memories or knowledge","memory update operations and persistence commands"],"categories":["memory-knowledge","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_2","uri":"capability://planning.reasoning.agent.planning.and.reasoning.decomposition","name":"agent planning and reasoning decomposition","description":"Teaches 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.","intents":["I want my agent to break down a complex task into smaller, manageable steps","I need the agent to reason about multiple solution paths and choose the best one","I want to implement backtracking or replanning when an action fails"],"best_for":["developers building agents for multi-step reasoning tasks (research, planning, debugging)","teams implementing hierarchical task decomposition for complex workflows","engineers designing agents that must handle ambiguous or open-ended user requests"],"limitations":["Planning depth and quality scale with LLM reasoning capability — no guarantee of optimal plans","Computational cost increases with search breadth (exploring multiple plans) and depth (multi-level decomposition)","Requires careful prompt engineering to elicit structured reasoning; patterns may not transfer across domains"],"requires":["LLM with strong reasoning capability (GPT-4, Claude 3+, or equivalent)","Understanding of prompt engineering for structured outputs (JSON, XML, etc.)","Ability to define task success criteria and evaluation metrics"],"input_types":["user task descriptions and goals","domain context and constraints","execution feedback and error messages"],"output_types":["task decomposition trees or step sequences","reasoning traces and decision justifications","replanning directives and alternative strategies"],"categories":["planning-reasoning","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_3","uri":"capability://planning.reasoning.multi.agent.coordination.and.communication","name":"multi-agent coordination and communication","description":"Describes 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.","intents":["I want to build a system where multiple agents with different specializations collaborate on a task","I need agents to communicate and share information during execution","I want to implement hierarchical agent teams with delegation and oversight"],"best_for":["teams building complex systems requiring multiple specialized agents (e.g., research, code generation, analysis)","developers implementing agent swarms or collective intelligence patterns","engineers designing systems where agents must negotiate or reach consensus"],"limitations":["Multi-agent coordination adds complexity in debugging and tracing execution flow","Message passing and synchronization introduce latency and potential deadlocks","Scaling to many agents (10+) requires careful orchestration to avoid exponential communication overhead","No built-in mechanisms for agent failure isolation or cascading error handling"],"requires":["Message queue or communication infrastructure (in-process, message broker, or API-based)","Shared context or state management system accessible to all agents","Orchestration framework or explicit coordination logic"],"input_types":["task specifications with multi-agent requirements","agent role definitions and capabilities","inter-agent message formats and protocols"],"output_types":["coordinated execution plans","agent communication logs and message traces","aggregated results from multiple agents"],"categories":["planning-reasoning","automation-workflow","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_4","uri":"capability://planning.reasoning.agent.autonomy.and.decision.making.loops","name":"agent autonomy and decision-making loops","description":"Teaches 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.","intents":["I want to understand the fundamental loop that makes an agent autonomous","I need to implement stopping conditions so agents don't run indefinitely","I want to add human oversight or approval gates to agent decisions"],"best_for":["developers building their first autonomous agent system","teams implementing safety constraints and human-in-the-loop oversight","engineers designing agents for high-stakes domains (finance, healthcare, infrastructure)"],"limitations":["Autonomy level must be carefully calibrated — too much leads to unsafe behavior, too little defeats purpose of agents","Stopping conditions are domain-specific and require careful design to avoid premature termination or infinite loops","Human oversight adds latency and may bottleneck agent throughput"],"requires":["Clear definition of agent goals and success criteria","Mechanism for observing environment state (APIs, sensors, logs, etc.)","Ability to execute actions and receive feedback"],"input_types":["user goals and task specifications","environment state observations","action execution results and feedback"],"output_types":["agent decisions and action selections","execution traces and reasoning logs","escalation requests or human approval prompts"],"categories":["planning-reasoning","automation-workflow","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_5","uri":"capability://planning.reasoning.agent.evaluation.and.testing.frameworks","name":"agent evaluation and testing frameworks","description":"Describes 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.","intents":["I need to measure whether my agent is actually solving tasks correctly","I want to compare different agent architectures or LLM choices objectively","I need to track agent performance over time and detect regressions"],"best_for":["teams deploying agents to production and requiring quality assurance","researchers comparing agent architectures or reasoning approaches","developers optimizing agent cost and latency"],"limitations":["Agent outputs are often non-deterministic, making traditional test assertions difficult","Task success metrics are domain-specific and may require human judgment to define","Evaluation can be expensive (requires multiple LLM calls) and slow (especially for long-running agents)","No universal benchmark suite — evaluation must be tailored to specific use cases"],"requires":["Clear definition of task success criteria","Test dataset or scenario suite representative of production use cases","Evaluation infrastructure (logging, metrics collection, result storage)"],"input_types":["agent execution traces and logs","task specifications and expected outcomes","ground truth or reference solutions"],"output_types":["performance metrics and success rates","cost and latency measurements","failure analysis and improvement recommendations"],"categories":["planning-reasoning","automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_6","uri":"capability://automation.workflow.error.handling.and.agent.failure.recovery","name":"error handling and agent failure recovery","description":"Teaches 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.","intents":["I want my agent to recover from transient failures without human intervention","I need to detect when an agent is stuck and try a different approach","I want to log and analyze agent failures for debugging and improvement"],"best_for":["developers building production agents that must handle unreliable external services","teams implementing resilient multi-step workflows","engineers designing agents for long-running or mission-critical tasks"],"limitations":["Distinguishing recoverable from fundamental failures requires domain knowledge and careful error classification","Retry strategies (backoff, exponential delays) add latency and may not always succeed","Some failures (e.g., invalid LLM outputs) may require human intervention or model fine-tuning","Error recovery adds complexity to agent logic and increases debugging difficulty"],"requires":["Structured error handling and logging infrastructure","Clear classification of error types and recovery strategies","Fallback mechanisms or alternative execution paths"],"input_types":["execution errors and exceptions","invalid or malformed agent outputs","timeout or resource exhaustion signals"],"output_types":["recovery actions and retry directives","error logs and failure analysis","escalation requests or human alerts"],"categories":["automation-workflow","safety-moderation","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_7","uri":"capability://text.generation.language.agent.prompt.engineering.and.instruction.design","name":"agent prompt engineering and instruction design","description":"Describes 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.","intents":["I want to write prompts that reliably guide agents toward correct behavior","I need to encode domain constraints and safety rules into agent instructions","I want to test different prompt variations and measure their impact on agent performance"],"best_for":["developers tuning agent behavior without retraining or fine-tuning models","teams implementing domain-specific agents with specialized instructions","engineers optimizing agent performance through prompt iteration"],"limitations":["Prompt effectiveness varies significantly across LLM models and versions","Prompts are brittle and may fail on edge cases or adversarial inputs","Prompt engineering is often empirical and domain-specific — patterns may not transfer","No systematic way to guarantee prompt compliance; LLMs may ignore instructions"],"requires":["Understanding of target LLM's capabilities and limitations","Ability to structure prompts with clear roles, tasks, and constraints","Testing infrastructure for A/B testing and prompt evaluation"],"input_types":["task descriptions and agent goals","domain constraints and safety rules","example inputs and expected outputs"],"output_types":["structured prompts with role, task, and constraint sections","prompt variations for A/B testing","performance metrics for prompt comparison"],"categories":["text-generation-language","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_8","uri":"capability://automation.workflow.agent.observability.and.execution.tracing","name":"agent observability and execution tracing","description":"Teaches 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.","intents":["I need to understand why my agent made a particular decision or took a wrong action","I want to visualize the agent's reasoning process for debugging and improvement","I need to audit agent decisions for compliance or safety verification"],"best_for":["developers debugging agent behavior and reasoning failures","teams implementing compliance and audit trails for agent systems","engineers optimizing agent performance by analyzing execution traces"],"limitations":["Comprehensive tracing adds overhead and increases storage requirements","Trace interpretation requires domain knowledge and manual analysis","Sensitive information in traces (API keys, user data) requires careful handling","Tracing granularity trade-off: too detailed is noisy, too coarse misses important context"],"requires":["Structured logging infrastructure (e.g., JSON logs, trace collectors)","Storage for execution traces (logs, databases, or trace services)","Visualization or analysis tools for trace inspection"],"input_types":["agent execution events (decisions, tool calls, results)","reasoning traces and intermediate outputs","error and exception information"],"output_types":["structured execution logs","trace visualizations and timelines","debugging reports and failure analysis"],"categories":["automation-workflow","safety-moderation","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-build-an-ai-agent-from-scratch__cap_9","uri":"capability://automation.workflow.agent.cost.optimization.and.resource.management","name":"agent cost optimization and resource management","description":"Describes 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.","intents":["I want to reduce the cost of running my agent without losing quality","I need to choose between different LLM models based on cost and capability trade-offs","I want to implement caching to avoid redundant LLM calls"],"best_for":["teams deploying agents at scale with cost constraints","developers optimizing agent economics for profitability","engineers managing multi-model agent systems"],"limitations":["Cost optimization often trades off against reasoning quality and accuracy","Caching strategies are task-specific and may not apply across domains","Model selection requires empirical testing to validate cost-quality trade-offs","Token counting and cost estimation are model-specific and change with pricing updates"],"requires":["Understanding of LLM pricing models and token counting","Ability to measure task success and cost metrics","Access to multiple LLM models for comparison"],"input_types":["agent execution traces with token counts","task specifications and success criteria","LLM pricing and capability data"],"output_types":["cost optimization recommendations","model selection guidance","caching strategies and implementation patterns"],"categories":["automation-workflow","agent-architecture"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":20,"verified":false,"data_access_risk":"high","permissions":["Understanding of function signatures and parameter types","Familiarity with JSON schema or equivalent structured definition format","Access to source code repository for concrete implementation examples","Understanding of token counting and context window limits for target LLM","Familiarity with vector databases or embedding services (optional for advanced patterns)","Storage infrastructure for persistent memory (database, vector store, or file system)","LLM with strong reasoning capability (GPT-4, Claude 3+, or equivalent)","Understanding of prompt engineering for structured outputs (JSON, XML, etc.)","Ability to define task success criteria and evaluation metrics","Message queue or communication infrastructure (in-process, message broker, or API-based)"],"failure_modes":["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","Planning depth and quality scale with LLM reasoning capability — no guarantee of optimal plans","Computational cost increases with search breadth (exploring multiple plans) and depth (multi-level decomposition)","Requires careful prompt engineering to elicit structured reasoning; patterns may not transfer across domains","Multi-agent coordination adds complexity in debugging and tracing execution flow","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=build-an-ai-agent-from-scratch","compare_url":"https://unfragile.ai/compare?artifact=build-an-ai-agent-from-scratch"}},"signature":"bd/8y6JKZIm1vQ7mY9Da9usk/hhbACj7jgzpuco/0wWm/lbpp3UrYbPQEMvhZGD0TYg6RPToI7prC7CS7sZcDA==","signedAt":"2026-06-20T16:11:38.969Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/build-an-ai-agent-from-scratch","artifact":"https://unfragile.ai/build-an-ai-agent-from-scratch","verify":"https://unfragile.ai/api/v1/verify?slug=build-an-ai-agent-from-scratch","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}