Tag Archives: Q&A

Agile’s Watergate

A relic of the Waterfall model is the construct of a “gate” process. In order for a project to achieve a milestone, the project/solution would need to achieve certain criteria that would allow it to go to the next phase of the project. For example, going from solidifying requirements in a Business Requirements Doc (BRD) to the software implementation phase.

In Agile, we leverage the Product Owner (PO) and the Product Backlog to determine what gets done and when. A Product Backlog item (PBI) may cover the full lifecycle of a Feature, from requirements to implementation. The Product Owner dictates acceptance of the PBI based on the status/transparency of the Backlog, such as the criticality of the Bugs linked to the PBI. Product quality and implemented functionality are transparent to the PO, who will determine the next steps such as release the software, and/or go through another iteration/sprint. Iterations are a defined cadence agreed to by the implementation team and the Product owner, typically, 2-week sprints.

Agile, Hybrid Environments: Opportunities for Synergy

Epics, Features, Product Backlog Items, and Tasks are object types in a Backlog that enable the PO and the team to link objects and plan over multiple sprints. Epics or Themes of Sprints are “high level”, potentially strategic initiatives. Features roll up into Epics as a part of several sprints. Either Epics or Features may be high enough level to link to Psydo Project Milestones for a product roadmap of deliverables, and solicitation outside the team.

Aggregation of Product Backlog Items, Effort Estimations, roll up into Features, and then up into Epics, which roughly equate to milestone timelines.

The “Definition of Done” (DoD) for a Product Backlog Item may require 0 outstanding Bugs with the severity of “Critical” linked to this PBI. The DoD criteria could be analogous to a traditional Quality Assurance gate.

Tasks that are production rollout activities, without a project plan, should be planned for in future sprints, akin to estimating when items may be completed in the proper sequence. Some of the Tasks may be placed conservatively in “early” sprints and may require items to be “pushed forward” after each of the iterations.

Amazon and Microsoft Drinking their own AI Chatbot Champagne?

A relatively new medium of support for businesses small to global conglomerates becomes available based on the exciting yet  embryonic [Chabot] / Digital Agent services.   Amazon and Microsoft, among others, are diving into this transforming space.  The coat of paint is still wet on Amazon Lex and Microsoft Cortana Skills.   MSFT Cortana Skills Kit is not yet available to any/all developers, but has been opened to a select set of partners, enabling them to expand Cortana’s core knowledge set.  Microsoft’s Bot Framework is in “Preview”  phase.  However, the possibilities are extensive, such as another tier of support for both of these companies, if they turn on their own knowledge repositories using their respective Digital Agents [Chabot]  platforms.

Approach from Inception to Deployment

  • The curation and creation of knowledge content may occur with the definition of ‘Goals/Intents’ and their correlated human utterances which trigger the Goal Question and Answer (Q&A) dialog format.  Classic Use Case.  The question may provide an answer with text, images, and video.
  • Taking Goals/Intents and Utterances to ‘the next level’ involves creating / implementing Process Workflows (PW).    A workflow may contain many possibilities for the user to reach their goal with a single utterance triggered.  Workflows look very similar to what you might see in a Visio diagram, with multiple logical paths. Instead of presenting users with the answer based upon the single human utterance, the question, the workflow navigates the users through a narrative to:
    • disambiguate the initial human utterance, and get a better understanding of the specific user goal/intention.  The user’s question to the Digital Agent may have a degree of ambiguity, and workflows enable the AI Digital Agent to determine the goal through an interactive dialog/inspection.   The larger the volume of knowledge, and the closer the goals/intentions, the implementation would require disambiguation.
    • interactive conversation / dialog with the AI Digital Agent, to walk through a process step by step, including text, images, and Video inline with the conversation.  The AI chat agent may pause the ‘directions’ waiting for the human counterpart to proceed.

Future  Opportunities:

  • Amazon to provide billing and implementation / technical support for AWS services through a customized version of their own AWS Lex service?   All the code used to provide this Digital Agent / Chabot maybe ‘open source’ for those looking to implement similar [enterprise] services.
  • Digital Agent may allow the user to share their screen, OCR the current section of code from an IDE, and perform a code review on the functions / methods.
  • Microsoft has an ‘Online Chat’ capability for MSDN.  Not sure how extensive the capability is, and if its a true 1:1 chat, which they claim is a 24/7 service. Microsoft has libraries of content from Microsoft Docs, MSDN, and TechNet.  If the MSFT Bot framework has the capability to ingest their own articles,  users may be able to trigger these goals/intents from utterances, similar to searching for knowledge base articles today.
  • Abstraction, Abstraction, Abstraction.  These AI Chatbot/Digital Agents must float toward Wizards to build and deploy, and attempt to stay away from coding.  Elevating this technology to be configurable by a business user.  Solutions have significant possibilities for small companies, and this technology needs to reach their hands.  It seems that Amazon Lex is well on their way to achieving the wizard driven creation / distribution, but have ways to go.  I’m not sure if the back end process execution, e.g. Amazon Lambda, will be abstracted any time soon.