Spotlight
PAGE: https://alison.com/course/building-bots-using-the-microsoft-bot-framework-revised
PROMISE: Grasp bot framework basics and Azure deployment logic in a short session.
WHO: Beginner bot developers who want a compact, free grounding in framework basics before committing to a stack.
WHYNOW: Stack choices for bot development keep shifting, and a short free course is a low-cost way to test the fundamentals before buying into paid Azure services.
DESC: Learn about the Microsoft Bot Frame work, to create bots, along with the Azure Bot service for increasing your bots intelligence.
BEFORE: Reading SDK docs without a clear picture of how a bot gets deployed.
BEFORE: Guessing which parts are framework vs. cloud service features.
BEFORE: No repeatable checklist for moving from local test to public endpoint.
AFTER: A named step-by-step path from framework to publish exists in notes.
AFTER: LUIS, Bot Connector, and Azure Bot Service roles are separated on paper.
AFTER: A simple publish checklist can be reused for the next bot project.
GAIN: A clear mental map of how the Microsoft Bot Framework builds a bot.
GAIN: Working definitions of LUIS intent parsing and Bot Connector delivery.
GAIN: A first-draft publish plan plus an honest read on when Azure Bot Service earns its keep.
STEP: Stuck at the start without a clear map of what the Bot Framework actually does. || Discuss how to use the Microsoft Bot Framework to create a bot || Start sketching a bot’s flow and components with a working mental model of the SDK.
STEP: Confused about how intents are parsed and messages are routed to users. || Explain the uses of LUIS and the Bot Connector || Know where LUIS slots in for intent recognition and how the Bot Connector handles delivery.
STEP: Unclear how to take a working bot from local to a reachable endpoint. || Explain how to publish your bot || Outline the publish path and registry steps before writing code for production.
STEP: Wander into paid Azure services without knowing why they matter for bot intelligence. || Explain the benefits of using the Azure Bot Service for your bot || Decide when managed Azure features replace local scaffolding in a production plan.
NOTFOR: Developers who already maintain production bot infrastructure and only need edge-case troubleshooting.
NOTFOR: Teams that require detailed observability setup, memory tuning, or load testing beyond a brief overview.
FACT: Average duration: 1.5-3 Hours (Contains Video)
FACT: CPD-accredited certificate available
FACT: Beginner level
COVERS: Discuss how to use the Microsoft Bot Framework to create a bot
COVERS: Explain the uses of LUIS and the Bot Connector
COVERS: Explain how to publish your bot
COVERS: Discuss connecting your bot to your users using the Bot Connector
COVERS: Explain the benefits of using the Azure Bot Service for your bot
COVERS: Discuss how to use the Azure Bot Service
COVERS: Deep Learning
COVERS: Microsoft Azure
COVERS: Automation
COVERS: Course Overview
COVERS: About This Course
MODULE: Building a Conversational Bot || The Bot Framework and New Bot Processes | Connecting to Users and Dialog Smarts
MODULE: Introducing the Azure Bot Service || Introducing The Azure Bot Service
What you are probably seeing
Your dashboards show spikes in token usage because agents re-read large context windows on every step instead of recalling prior facts.
Support tickets increase as agents repeat failed actions because the system lacks a shared record of what happened in previous sessions.
Why it happens
Standard agent loops use ephemeral memory that clears after each request, forcing the system to rely on brittle prompt stuffing.
Observability tools often log traces but do not parse them into structured state, making it impossible to query past decisions later.
What changed recently
2026-10-07 · is building ai agents worth it
Teams can add Memori to fix this gap:
• Agent-native SQL memory infra.
• Converts traces into persistent state.
• Includes recall and governance.
Source: autocomplete
Step by step
1. Map your current agent workflow to identify which specific facts and user preferences must persist between interactions.
2. Design a schema that stores these key details in a durable database layer accessible by your agent logic.
3. Implement a recall mechanism that retrieves relevant stored state before the agent plans its next action.
4. Add logging hooks that write significant state changes back to the database after each step completes.
5. Test the pipeline by simulating a multi-turn conversation to verify that the agent remembers previous context correctly.
Ways to go further
Start here: Building Bots Using The Microsoft Bot Framework For when you want to learn bot development basics. Open the free course on Alison ↗
Better fit: COBIT Framework – Foundations of IT Governance For when you need a reference for IT governance principles. Open the free course on Alison ↗
Affiliate links: CHEAPUNO may earn a commission if you buy through them, at no extra cost to you.
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