CypherMonkeyAI omni-channel lab

A lifelong-learner lab on AI omni-channel agents, including voice and the contact center. Free to read. Nothing sold.

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Finish in 10 minutes

Read only these opening rooms, export one diagram, and stop.

  1. How this learning lab works — This site is a free learning lab about AI omni-channel agents.
  2. Tokens, not words — A model reads tokens, which may be parts of words.
  3. A job, a shape, and a test — A wish is not a job. Name the reader and the done check.
  4. Why retrieve — A model does not contain your private notes.
  5. An agent is a loop — An agent here means the model can choose a tool, see the result, and continue.
  6. The problem MCP names — Tools written for one app are hard to reuse in another.
  7. Omni-channel, for a learner — Omni-channel here means one assistant pattern that can meet a person on more than one channel.
  8. One GPU, then a cluster — A GPU is a processor often used for the matrix math in model training and serving.
  9. Speech in, a model, speech out — LiveKit's pipeline page names a cascaded pipeline: speech-to-text, then a language model, then text-to-speech.
  10. Transcription of a recording — Wikipedia defines speech analytics as analyzing recorded calls to gather customer information.
  11. Handoff to a person — A handoff in this lab means a person continues the turn.
  12. Words for this ecosystem — Wikipedia defines speech analytics as analysis of recorded calls.

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Sources and go deeper

Programming foundations

Files, HTTP, JSON, and a notebook you can re-run. No model call is required to start.

  1. How this learning lab works · L1 · 10 min
  2. Programs, files, and a notebook · L1 · 15 min
  3. Python values and functions · L1 · 20 min
  4. HTTP and JSON · L1 · 20 min
  5. Secrets stay out of the file · L1 · 15 min
  6. Tests and a small eval · L2 · 20 min

Sources and go deeper

Room files: T1-R1 sources.json · T1-R2 sources.json · T1-R3 sources.json · T1-R4 sources.json · T1-R5 sources.json · T1-R6 sources.json

How models work

Tokens, context, sampling, and why a fluent sentence can still be false.

  1. Tokens, not words · L1 · 15 min
  2. Next-token prediction · L1 · 15 min
  3. Context windows · L2 · 15 min
  4. Sampling · L2 · 15 min
  5. Hallucination · L2 · 15 min
  6. Model cards and ids · L2 · 15 min

Sources and go deeper

Room files: T2-R1 sources.json · T2-R2 sources.json · T2-R3 sources.json · T2-R4 sources.json · T2-R5 sources.json · T2-R6 sources.json

Prompting and evals

A job, a shape, and a test. Change one thing and keep the score.

  1. A job, a shape, and a test · L1 · 15 min
  2. Messages · L2 · 15 min
  3. Schemas · L2 · 20 min
  4. A golden set · L2 · 20 min
  5. Latency and a toy budget · L2 · 15 min
  6. Prompt diff · L2 · 15 min

Sources and go deeper

Room files: T3-R1 sources.json · T3-R2 sources.json · T3-R3 sources.json · T3-R4 sources.json · T3-R5 sources.json · T3-R6 sources.json

Retrieval

Find a passage, then answer from it. Measure whether the passage was found.

  1. Why retrieve · L1 · 15 min
  2. Chunks · L2 · 15 min
  3. Embeddings · L2 · 20 min
  4. Retrieve, then read · L2 · 15 min
  5. Cite or refuse · L2 · 15 min
  6. Failure modes · L2 · 20 min

Sources and go deeper

Room files: T4-R1 sources.json · T4-R2 sources.json · T4-R3 sources.json · T4-R4 sources.json · T4-R5 sources.json · T4-R6 sources.json

Agents

A loop that may call a tool, with a schema, a step cap, and a trace.

  1. An agent is a loop · L1 · 15 min
  2. Tools and schemas · L2 · 20 min
  3. Step caps and traces · L2 · 15 min
  4. Human approval · L2 · 15 min
  5. ReAct, at learning depth · L2 · 20 min
  6. When not to use an agent · L2 · 15 min

Sources and go deeper

Room files: T5-R1 sources.json · T5-R2 sources.json · T5-R3 sources.json · T5-R4 sources.json · T5-R5 sources.json · T5-R6 sources.json

Model Context Protocol

A way for a host to talk to tool servers. Confirm the current specification before you build.

  1. The problem MCP names · L1 · 15 min
  2. Host, client, and server · L2 · 15 min
  3. Tools, resources, and prompts · L2 · 20 min
  4. Transports · L2 · 15 min
  5. Consent and roots · L2 · 20 min
  6. A notes server on paper · L3 · 25 min

Sources and go deeper

Room files: T6-R1 sources.json · T6-R2 sources.json · T6-R3 sources.json · T6-R4 sources.json · T6-R5 sources.json · T6-R6 sources.json

Voice and omni-channel agents

One learning model of voice, chat, and messaging, with a handoff to a person. Not a deployment claim.

  1. Omni-channel, for a learner · L1 · 15 min
  2. The voice loop · L2 · 20 min
  3. A latency budget you label as a lesson · L2 · 15 min
  4. Chat and messaging beside voice · L2 · 15 min
  5. Handoff to a person · L2 · 20 min
  6. What this lab will not claim · L1 · 10 min

Sources and go deeper

Room files: T7-R1 sources.json · T7-R2 sources.json · T7-R3 sources.json · T7-R4 sources.json · T7-R5 sources.json · T7-R6 sources.json

Compute, cloud, and a capstone

What a GPU cluster is for, how a cloud API differs, and a small grounded assistant you can actually finish.

  1. One GPU, then a cluster · L2 · 15 min
  2. Colossus as a public story · L2 · 15 min
  3. Cloud endpoints · L2 · 20 min
  4. Logs, regions, and data you may use · L2 · 15 min
  5. A one-page policy · L2 · 20 min
  6. Capstone and certifications · L3 · 25 min

Sources and go deeper

Room files: T8-R1 sources.json · T8-R2 sources.json · T8-R3 sources.json · T8-R4 sources.json · T8-R5 sources.json · T8-R6 sources.json

Voice AI agent stack

Speech in, a model, speech out, and the network that carries the audio.

  1. Speech in, a model, speech out · L1 · 20 min
  2. Turn-taking and barge-in · L1 · 20 min
  3. Latency budgets you label · L2 · 15 min
  4. Telephony and WebRTC · L2 · 20 min
  5. LiveKit Agents and Pipecat · L2 · 20 min
  6. A hosted voice agent, from the xAI docs · L3 · 25 min

Sources and go deeper

Room files: T9-R1 sources.json · T9-R2 sources.json · T9-R3 sources.json · T9-R4 sources.json · T9-R5 sources.json · T9-R6 sources.json

Speech analytics and quality

Transcription, sentiment, a QA score, and redaction, with the pitfalls named.

  1. Transcription of a recording · L1 · 15 min
  2. Sentiment is a label · L2 · 15 min
  3. A QA score needs a rubric · L2 · 20 min
  4. Redaction · L2 · 20 min
  5. Pitfalls · L3 · 20 min

Sources and go deeper

Room files: T10-R1 sources.json · T10-R2 sources.json · T10-R3 sources.json · T10-R4 sources.json · T10-R5 sources.json

AI agent safety

Handoff, guardrails, consent as education, and a test you can re-run.

  1. Handoff to a person · L1 · 15 min
  2. Guardrails · L2 · 20 min
  3. Consent and recording, as education · L2 · 20 min
  4. Evaluation and testing · L2 · 20 min
  5. What this chapter will not claim · L3 · 15 min

Sources and go deeper

Room files: T11-R1 sources.json · T11-R2 sources.json · T11-R3 sources.json · T11-R4 sources.json · T11-R5 sources.json

Contact-center ecosystem

A glossary, a mind map, sister systems, an integration map, bridges, and future hooks.

  1. Words for this ecosystem · L1 · 15 min
  2. A mind map · L1 · 15 min
  3. Sister technologies · L2 · 20 min
  4. An integration map · L2 · 20 min
  5. Bridges across platforms · L2 · 20 min
  6. Future hooks · L3 · 15 min

Sources and go deeper

Room files: T12-R1 sources.json · T12-R2 sources.json · T12-R3 sources.json · T12-R4 sources.json · T12-R5 sources.json · T12-R6 sources.json