Tool Calling
Typed tool interfaces, narrow parameters and per-tool authorisation.
4 to work through
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beginner
A team gives an assistant five tools. A task that needs four tool calls costs roughly six times a plain answer and takes about 9 seconds end to end, and the team expected the tools to be nearly free. What actually happens on each tool call?
3 min answer -
intermediate
An agent resolves support tickets in an average of 8 tool calls. A downstream inventory tool that used to answer in 80 ms starts taking 6 s but still returns correct results. Nothing errors and no circuit breaker trips. What happens, second by second?
3 min answer -
advanced
A commerce platform exposes tools for a model to call. How should the tool interface be designed, and what differs from designing an API for developers?
2 min answer -
advanced
An agent can query the customer database, send emails and issue refunds. What is your security design?
2 min answer
3 terms in this topic
Agent Tool Authorisation
Enforcing that a tool invoked by a model executes with the requesting user's permissions rather than the application's, and that consequential action…
patternTool Result Budget
A hard cap on the tokens any single tool may return into the model's context, with pagination and summarisation behind it, so that one unlucky query …
practiceTool Schema Design
Defining the tools available to a model — names, descriptions, parameters and errors — in a way that makes correct selection likely.
Neighbouring topics
AI-Era Architecture
General material on architecting systems that include models.
LLM Application Architecture
The shape of a production system with a model in the request path.
RAG Architecture
Retrieval, grounding, citation and the permissions RAG can enforce.
Vector Databases
Approximate nearest-neighbour search, filtering and re-indexing.
Embeddings
Dense representations, model coupling and the migration they imply.
Chunking & Retrieval
Structure-aware splitting, hybrid search and why chunking dominates quality.
Reranking
Cross-encoders improving precision more than a bigger embedding model.
Model Selection
Capability, latency, cost and the evaluation that decides between them.
AI Gateways
Centralised routing, keys, quotas, caching, logging and safety policy.
Prompt & Version Management
Prompts as reviewed, versioned, evaluated production configuration.
Agent Architectures
Loops, planning, memory and the boundaries an agent must not cross.
Multi-Agent Systems
Coordination, hand-off and whether more agents actually help.
LLM Evaluation
Held-out sets, rubric judging, CI gates and production sampling.
AI Observability
Logging prompts, versions, retrieved context and cost per request.
Guardrails
Deterministic checks on input and output that fail closed.
Prompt Injection Defence
Breaking the private-data, untrusted-input, outbound-channel combination.
AI Cost Management
Token accounting, routing, caching and the context-window budget.
Human in the Loop
Gating by reversibility and blast radius, and avoiding approval fatigue.
ML Platform
Feature stores, training pipelines, registries and deployment.