← Back
Home › Systems › Agentic AI

support-agent

customer support agent

Intercom Fin

SystemTechniques · 23Primitives · 85 support-agent Citation groundingagentic-ai · medium: Attaching answer spans to retrieved source passages and verifying each claim is supportedHuman-in-the-loop approvalagentic-ai · medium: Classify each proposed action as auto, ask or deny; park asks as durable pending records a human approves, edits or rejects, executing once, with timeouts that deny.›Prompt injection defenseagentic-ai · medium: Treat every untrusted string as data, track where values came from, and make tool calls and rendered output depend on provenance, not on what the model says.›User simulationagentic-ai · medium: LLM-driven simulated users following scripted goals to test conversational agentsDialogue state trackingmachine-learning · medium: Keep a structured belief of the user's goal (slot → value, plus requests) and update it every turn from NLU output, the old state and the last system action.›Intent classification and slot fill…machine-learning · medium: Turn one utterance into an intent label and typed slot values with a joint model: a sentence-level classifier plus a token-level BIO tagger on a shared encoder.›LLM-as-judgemachine-learning · medium: Grade open-ended outputs with a language model under a rubric, then correct its known biases by order swapping, voting and calibration against human labels.›Text chunkingmachine-learning · low: Cut documents into token-bounded pieces along structure boundaries, keeping offsets and section paths, so each piece embeds well and can be cited.›Embeddingsmachine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.›BM25 rankingsearch-sort · low: The standard lexical relevance score: IDF-weighted term frequency with saturation (k1) and length normalization (b); default in Lucene, Elasticsearch and SQLite FTS5.›Agent evaluationagentic-ai · unknown: Agent loopagentic-ai · unknown: Agent memoryagentic-ai · unknown: Constrained decodingagentic-ai · low: Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.›Context window managementagentic-ai · low: Keep an agent's growing history inside the model's window by truncating, clearing and summarizing old turns in a cheap-to-lossy ladder, at turn boundaries.›Tool callingagentic-ai · low: The model emits a structured call (id, name, JSON arguments) instead of prose; your code validates, runs it and returns a result tagged with the same id.›Idempotency keysfinance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.›Hybrid search and rank fusionsearch-sort · low: Merge a keyword (BM25) result list and a vector (ANN) list into one ranking, by ranks (RRF) or by normalized scores (convex combination).›Authorization models (RBAC, ABAC, R…security · low: How to decide who may do what to which object; roles for coarse rules, relationships for sharing, and checks on every object.›Capability-based security (object c…security · low: Authority is a handle you hold, not an identity you have: code can only act on objects it was explicitly given handles to, and handles can only be narrowed and passed on.›Fuzzy string matchingsearch-sort · low: Edit distance, Jaro-Winkler and trigram indexes for approximate name and address matchingStatechartsgraphics · low: State machines with nesting, parallel regions, history and entry/exit actions, so UI and animation logic stays linear in size instead of exploding.›Dual control approvalsecurity · low: Four-eyes / maker-checker approval with quorum policies for sensitive operations+ 16 more techniques+ 69 more primitives

Similar systems

Share the most building blocks with this one.

deep-research 30 %ai-co-scientist 26 %conversational-agent-platform 26 %llm-guardrails 25 %rag 25 %