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AI Tool-Use Engineering: Function Calling, Tool Routing, Permission Boundaries, Execution Control, and Verification - Softcover
Synopsis
AI Tool-Use Engineering is a guide to the interface between a language model's reasoning and the systems it can actually affect: function calling, tool routing, permission boundaries, execution control, and verification. This is the layer where a model's decision to act becomes a real action with real consequences.
Eleven chapters cover the full tool-use stack:
• Tool-using AI architecture: tool catalog design, schemas, routing, and a separated execution layer
• Function calling interfaces: typed arguments, interface-level validation, typed outputs, actionable error surfaces
• Tool routing and selection: intent mapping, capability boundaries, cost-aware routing, fallback chains
• Permission boundaries: scopes, least privilege, execution-layer-only credentials, informative approval gates
• Execution control: tool-specific timeouts, layered resource limits, runtime-enforced sandboxing, clean cancellation
• Result verification: automatic schema checks, genuinely independent cross-validation, confirmed side effects
• Reliable multi-step tool workflows: externally tracked state, explicit dependencies, step-level retry, compensation
• Security and abuse resistance: injection defense, misuse detection, exfiltration prevention, execution-layer policy
• Testing tool-using systems: deterministic mocks, failure injection, decision-path coverage, regression triggers
• Production tool infrastructure: a single tool registry, explicit versioning, decision-inclusive observability
• Cost and latency management: per-call attribution, latency budgets, volatility-aware caching, batching
Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario.
Four appendices cover architectural comparisons, a tool-use maturity model, a failure-mode reference organized by symptom, and common questions.
Written for engineers building AI agents, copilots, and any system where a model's decisions translate into real actions.
"synopsis" may belong to another edition of this title.
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AI Tool-Use Engineering (Paperback)
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Paperback. Condition: new. Paperback. AI Tool-Use Engineering is a guide to the interface between a language model's reasoning and the systems it can actually affect: function calling, tool routing, permission boundaries, execution control, and verification. This is the layer where a model's decision to act becomes a real action with real consequences. Eleven chapters cover the full tool-use stack: - Tool-using AI architecture: tool catalog design, schemas, routing, and a separated execution layer- Function calling interfaces: typed arguments, interface-level validation, typed outputs, actionable error surfaces- Tool routing and selection: intent mapping, capability boundaries, cost-aware routing, fallback chains- Permission boundaries: scopes, least privilege, execution-layer-only credentials, informative approval gates- Execution control: tool-specific timeouts, layered resource limits, runtime-enforced sandboxing, clean cancellation- Result verification: automatic schema checks, genuinely independent cross-validation, confirmed side effects- Reliable multi-step tool workflows: externally tracked state, explicit dependencies, step-level retry, compensation- Security and abuse resistance: injection defense, misuse detection, exfiltration prevention, execution-layer policy- Testing tool-using systems: deterministic mocks, failure injection, decision-path coverage, regression triggers- Production tool infrastructure: a single tool registry, explicit versioning, decision-inclusive observability- Cost and latency management: per-call attribution, latency budgets, volatility-aware caching, batching Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario. Four appendices cover architectural comparisons, a tool-use maturity model, a failure-mode reference organized by symptom, and common questions. Written for engineers building AI agents, copilots, and any system where a model's decisions translate into real actions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798175815444
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AI Tool-Use Engineering
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AI Tool-Use Engineering: Function Calling, Tool Routing, Permission Boundaries, Execution Control, and Verification
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AI Tool-Use Engineering
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AI Tool-Use Engineering : Function Calling, Tool Routing, Permission Boundaries, Execution Control, and Verification
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Taschenbuch. Condition: Neu. Neuware - AI Tool-Use Engineering is a guide to the interface between a language model's reasoning and the systems it can actually affect: function calling, tool routing, permission boundaries, execution control, and verification. This is the layer where a model's decision to act becomes a real action with real consequences. Eleven chapters cover the full tool-use stack: - Tool-using AI architecture: tool catalog design, schemas, routing, and a separated execution layer- Function calling interfaces: typed arguments, interface-level validation, typed outputs, actionable error surfaces- Tool routing and selection: intent mapping, capability boundaries, cost-aware routing, fallback chains- Permission boundaries: scopes, least privilege, execution-layer-only credentials, informative approval gates- Execution control: tool-specific timeouts, layered resource limits, runtime-enforced sandboxing, clean cancellation- Result verification: automatic schema checks, genuinely independent cross-validation, confirmed side effects- Reliable multi-step tool workflows: externally tracked state, explicit dependencies, step-level retry, compensation- Security and abuse resistance: injection defense, misuse detection, exfiltration prevention, execution-layer policy- Testing tool-using systems: deterministic mocks, failure injection, decision-path coverage, regression triggers- Production tool infrastructure: a single tool registry, explicit versioning, decision-inclusive observability>Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario. Four appendices cover architectural comparisons, a tool-use maturity model, a failure-mode reference organized by symptom, and common questions. Written for engineers building AI agents, copilots, and any system where a model's decisions translate into real actions. Seller Inventory # 9798175815444
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AI Tool-Use Engineering (Paperback)
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Paperback. Condition: new. Paperback. AI Tool-Use Engineering is a guide to the interface between a language model's reasoning and the systems it can actually affect: function calling, tool routing, permission boundaries, execution control, and verification. This is the layer where a model's decision to act becomes a real action with real consequences. Eleven chapters cover the full tool-use stack: - Tool-using AI architecture: tool catalog design, schemas, routing, and a separated execution layer- Function calling interfaces: typed arguments, interface-level validation, typed outputs, actionable error surfaces- Tool routing and selection: intent mapping, capability boundaries, cost-aware routing, fallback chains- Permission boundaries: scopes, least privilege, execution-layer-only credentials, informative approval gates- Execution control: tool-specific timeouts, layered resource limits, runtime-enforced sandboxing, clean cancellation- Result verification: automatic schema checks, genuinely independent cross-validation, confirmed side effects- Reliable multi-step tool workflows: externally tracked state, explicit dependencies, step-level retry, compensation- Security and abuse resistance: injection defense, misuse detection, exfiltration prevention, execution-layer policy- Testing tool-using systems: deterministic mocks, failure injection, decision-path coverage, regression triggers- Production tool infrastructure: a single tool registry, explicit versioning, decision-inclusive observability- Cost and latency management: per-call attribution, latency budgets, volatility-aware caching, batching Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario. Four appendices cover architectural comparisons, a tool-use maturity model, a failure-mode reference organized by symptom, and common questions. Written for engineers building AI agents, copilots, and any system where a model's decisions translate into real actions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798175815444
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