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conversational-agent-platform

enterprise chat and voice bot builder

Dialogflow CX, Amazon Lex, Azure Bot Service

SystemTechniques · 36Primitives · 122 conversational-agent-pl… Human-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 agentsDeterministic simulation testingdistributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.›Primary-backup replicationdistributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.›Dialogue 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.›Entity linkingmachine-learning · medium: Map each mention to a knowledge-base id, or to NIL, by generating candidates from an alias table, scoring them on prior and context, and thresholding.›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.›Text normalizationmachine-learning · low: Rewrite messy text into one canonical form for matchers and models, or into spoken words for speech output (and back), keeping a map to the original offsets.›Agent evaluationagentic-ai · unknown: Agent loopagentic-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.›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.›Hdr histogramdata-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentilesProcess supervisionhardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.›End of turn detectionmachine-learning · low: Deciding when a speaker has finished (silence plus prosody/semantic models) to start a reply with low latencyVoice activity detectionmachine-learning · low: Per-frame speech/non-speech decision with smoothing, from an energy threshold over a tracked noise floor up to small neural nets.›Sip signalingmedia · low: SIP call setup, dialogs, SDP offer/answer and registrar/proxy routingJSON Schema validationsecurity · low: Compile a JSON Schema once, then check every untrusted document against it, within hard size and depth limits, and report failures as JSON Pointers.›Mutual TLSsecurity · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.›Graceful drainingsystems · low: On a termination signal, stop taking new work, finish in-flight work until a deadline, tell clients to reconnect elsewhere, then exit; clients resume from a token.›Hierarchical state machinesystems · low: Nest states so a substate inherits its parent's reactions and only overrides what differs, with ordered entry and exit actions along the path.›Statechartsgraphics · low: State machines with nesting, parallel regions, history and entry/exit actions, so UI and animation logic stays linear in size instead of exploding.›+ 29 more techniques+ 106 more primitives

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