Towards a Framework for AI Agent Design, Part 3
In Part 1 of this series on a Framework for AI Agent Design, we looked at a life cycle planning model for your agentic projects. Part 2 looked at the different organizational elements that you have to understand and orchestrate in order to create successful AI Agent projects.
"Ok," you might say. "I'm ready to get some work done. How do I design AI agents to do the work?"
This is where the rubber meets the road. Since this is a framework discussion, we won't be going into the individual steps that you design and build as you create your AI agent in your platform of choice. For example, there is no right answer to questions like "How many steps should I have in my email response agent workflow?"
But what is useful is to think about what the goal is in developing your agentic AI solution. There are three sub-frameworks that can help guide you as you build your AI agents.
Outcomes are Great…
But You Have to Break Them Down into Deliverables and Tasks to Get There
Every company has a big collective goal they're trying to achieve on some time frame. There is some outcome they are hoping to bring about. Usually this is done quarterly in order to remain focused and"do-able". Many people, especially in technology companies, adopt the popular OKR (Objectives and Key Results) framework to assist in this exact kind of bigger picture planning and performance.
Wouldn't it be great if we could spin up an AI Agent that would just do all our OKRs for us?
Not going to happen.
Not for a while, anyway. But we can be sure of one thing: When this Mega AI Agent arrives, it will be composed of dozens, hundreds, maybe thousands of small, discrete AI Agents performing specific workflows. These will be coordinated to achieve the higher order outcomes.
So this gives us our place to start in our AI Agent planning: Not with the largest outcome, but with the smallest.
In other words, AI Agent builders need to carry out a research and analysis of the domain, the department, and the workers they seek to help. The analysis consists of identifying their workflows and deliverables, and then breaking them down into their smaller and smaller component parts.
A good model of this breakdown comes from basic physical science: What is a thing made up of? We decompose it like this:
1. An object
2. Materials and aggregates
3. Compounds
4. Elements
5. Atoms
Fortunately, in the world of work, we have a similar breakdown structure. This is our guide in analyzing the workflows we want to automate. It goes from large to small, from long-term to short-term, from slow to fast.
The Taxonomy of Work
Here is the structure you can follow in your workflow analysis. Start at the top and break it down into its component parts. Keep going down through the stack. Sometimes these components are workflows. Often they are some form of documentation. They both work in figuring out the relationship of smaller steps rolling up to the bigger task.
Outcome or Objective from your OKR
The big achievement that requires a department of people and projects to achieve
Projects
The big efforts carried out by a team
Documents / Deliverables
The information products, reports, and status/communication messages that mark progress and guide the team
Pro tip: Think broadly. Everything is a deliverable. It can be a formal report. It can also be a regular status Slack message.
Tasks
The activity an individual carries out, usually contributing to a deliverable, or being tracked by one
What we usually call "the daily work" for a specific job
Triggers / Inputs / Actions
The smallest steps or micro-tasks that make up a task
These are usually not thought about much. But they are atomic foundation of a workflow. This is what we want to pull out and work on.
When we break work down into tasks and inputs/actions, we find we have defined:
clearly scoped activities
with known inputs and outputs
expected behaviors
definitions of good and bad
a definition of done
These are ideal workflows for AI Agents.
In other words, when you start to implement AI Agents in your work, start at the bottom of the stack, and build those first. This is where you will get your fastest, earliest efficiencies.
Agents Transform Data and Documents from One State to Another
When designing an individual AI Agent, it's useful to think of the agent as sitting in the middle of a process or structure that has three parts. It's shaped like a barbell:
Starting data set or document, the inputs at rest
The AI Agent, the transformer, the machine with moving parts
Ending data set or document, the outputs at rest
The AI Agent's job is to change the data from one state to another. The output is a more valuable state. It's the thing we want, so we can take it and do more work with it.
So what kinds of data transformations can AI Agents do efficiently?
Organize and categorize
Aggregate
Analyze
Synthesize
Standardize
Summarize
Expand and enrich
Check and validate
Group and ungroup
Find patterns
Find missing pieces
When we break down at tasks this way, we have a formula that's not complicated, but can be mixed and matched to define almost any AI Agent we need at the task level.
So send your AI Agents to the work gym and have them start lifting barbells.
Design "Instructional Pressures" on the Agent to Keep It on Task
A third design pattern for creating effective AI Agents is to borrow a concept from instructional design: Corrective feedback.
When students learn, they are going through a process just as described above. How do we help them learn a thing correctly? This is where comprehension checks, tests, demonstration activities, questioning, and coaching and teaching come in to play. If a student is going off into misunderstanding or in the wrong direction, feedback applies instructional pressure to keep them on the right learning path. Feedback helps them get to the desired outcome: They learned the thing. They did the work.
We want to design checkpoints in our agentic workflows where there is the same opportunity for checking and corrective feedback if necessary.
Algorithmic Checkpoints
We can program algorithmic error checking into the AI Agent itself. That is, we can add logic to the agent where it will check its own progress and take corrective action. This is becoming easier and more robust with the latest LLMs that have advanced reasoning capabilities.
We see this when we submit a question and watch the agent play back the work planning steps it is creating for itself as it works towards the final answer. Some examples of built in algorithmic correction are asking the agent to compare its work to an example, or to reason backwards from an outcome, or to generate multiple options for itself, and choose the "best" one.
Human in the Loop
The second way to get corrective feedback to an AI Agent is to build in checkpoints for human oversight. This is HITL: Human in the loop. These are points in the workflow where the Agent pauses and a human has the chance to review, catch mistakes, and offer correction, and ultimately approval. For long or more complicated agentic workflows, these human in the loop moments are scheduled happen every time the agent runs. For example, a worker may make an AI Agent that prepares a draft document for review, and will not send out the document until a human has given it approval. Another pattern to use in AI Agent design is to define escalation scenarios. That is, define a knowledge, skill or confidence threshold in the Agent's instructions. When the Agent self-assesses that it has hit that limit, it stops and seeks human input. Both of these are examples of what I call "instruction presssure" that will help keep your AI Agents from making critical mistakes or hallucinations.
So there you have it: Three sub-frameworks for designing individual AI Agents:
Break the work down into its smallest tasks and actions, and start building your army of agents there.
Design your AI agent's work to consist of taking data and documents and changing them into new, more valuable forms
Build in ways for corrective feedback to the agent to be part of the standard logic and workflow so the agent can do the work, and not you.
Combined with Framework Part 1, the life cycle planning model and Framework Part 2, the component and stakeholder model, and this is a draft of a complete framework for guiding AI agent design at the high level.
What do you think? Do you agree? Disagree?
Comments and feedback welcome, and thanks for reading.
https://get.mindstudio.ai/t3p8e2ypz6tp
Towards a Framework for AI Agent Design, Part 2
In Part 1 of this series on a Framework for AI Agent Design, we looked at a life cycle model for your agentic projects. Looking at the design process chronologically or sequentially can indeed help you see the big picture so you can make the big moves.
But you are right to ask: "What exactly is IN an AI Agent?"
Successful AI agents are composed of tangible and intangible components
Now let's look at a different aspect or side to this framework. It has a different focus: Consider the functional components of AI agent design.
When we shop at Ikea, we bring back boxes filled with a huge number of pieces that need to be gathered, verified, and then put together. In the kitchen, we might follow a recipe. It gives a list of required ingredients and how they should be prepped and then cooked.
AI agent design deals with screens and content. But most of what goes into our process are the intangible but vital components of a software system. These components are processes, resources, factors and configurations, and strategies.
The AI agent design process is a series of lenses for examining the work
Using the seven lenses below, we make a comprehensive inspection of the big picture environment for the AI agent. The decisions we make for each form our AI product and design requirements. Deciding on each of the following AI components is Part 2 of our framework.
Users
The whole point of AI agents is for people to use them; agents do work for humans, under their review. So start with understanding the people who need or want the AI agent solution. Expand this to all the key stakeholders: Workers, users, managers, executives, other internal stakeholders, and the business's customers and audiences. Questions to answer include:
What is the work they do? What is their concept of their job or function?
What's currently working well? Not well?
What constitute good versus bad work outcomes?
What are their hopes for AI implementation? Their fears?
What's the ideal solution in their view? Why do they think that?
Pro tip: It's not too early to start thinking of AI agents as junior workers alongside human workers. What is the best user persona of the AI Agent itself?
Data
AI agents and the large language models that power them excel at finding, ingesting, analyzing, and summarizing large amounts of information. Most companies are sitting on huge quantities of data: document servers, email, slack channels, web and app analytics, CRM systems, sales and customer support systems. It may not be available or structured effectively, but the agents may need to access it. An audit of the data is called for:
What data exists? What data is missing?
How good is it: How timely? Is it structured for AI understanding?
How is data maintained? Deleted?
How do AI agents need to access it, transform it, store it?
Context
This is the big one. Context starts at a very general, global level and includes:
Background information on the business
The industry
Competitors
Economic and financial trends
This sort of context might show up as "system prompts" for AI Agents that all employees and agents start with.
From there, context becomes increasingly specific, granular, and local. Each level has its own context, which needs to be documented as references or prompts:
Department context
Job context
Task context
Goals and metrics exist at all of these levels as well. The strategy for context is for the company or an individual (or even an AI agent) to be able to generate repeatedly high quality, accurate, effective context and prompting for the AI agent.
Workflows
Workflows describe the actual work to be done, and how it is sequenced. Finding and documenting workflows is the main focus of the discovery or strategy phase of an agent project. There are multiple frameworks that offer similar methods for doing this: developing product sense, user research, mental models, listening tours, contextual inquiry. Aspects to be documented include:
Starting point of the workflow
Prior states and triggers
Inputs and outputs
Tasks, subtasks, sequences, and transformations
Errors & null conditions
Integrations with other systems
How many AI agents do you need to complete the workflow?
If multiple agents, what will their roles be and what are the sub-agents?
UX Design
AI agents may become autonomous, but humans and agents must still interact. If the agent UI is confusing, incomplete or hard to use, the expected performance gains will not be achieved. What is the optimal intended experience of using the AI Agent and completing the workflow successfully? While interaction modalities are still somewhat limited with AI Agents, this is evolving rapidly beyond the text query box. What can you do to raise the Agent experience beyond crude completion? Can you make it easy to show, sell, learn, use, maintain?
AI Models and Configurations
Choices need to be made at the AI model level regarding which model(s) to use. Also what associated internal and external computing services to use. A strategy for the AI infrastructure would develop answers to questions such as:
What AI language model or models should be used?
Which are allowed? Which are forbidden?
What are the capabilities and costs of the selected model?
What happens if a better model is released, or compute fees go up?
What APIs and tools does the AI agent need to connect to?
Do additional ones need approval, purchase, and integration?
Who or what will use and store these inputs and outputs?
What is the plan for when the underlying LLM must be changed?
Pro tip: What are the triggers that elevate the agent's work for human review? What are those scenarios?
Cybersecurity and Usage Policies
Agents are AI and software. Most of this software exists as cloud services. All of these can be hacked, hijacked, subjected to prompt insertion attacks, and much more. The cybersecurity concerns of AI Agents are just emerging, but mission critical data, systems, and workflows must be totally secure. The security of proprietary and confidential data must be maintained for competitive and legal reasons. Policies must be developed and protections put in place to constrain the access rights and scope of impact of the LLMs, the AI agents, and the human workers.
These are the seven functional components of a proposed framework for AI agent design. They add to the six life cycle stages outlined in Part 1.
Follow me for Part 3, where we look at how these two sides of AI agent design might work together.
What are your thoughts on how we can approach AI Agent design for maximum effectiveness and success?
Comments and criticisms welcome.
https://get.mindstudio.ai/t3p8e2ypz6tp
Towards a Framework for AI Agent Design, Part 1
Everyone is interested in AI Agents these days. They hold great promise in automating work, saving time, and delivering better, faster service. How can we make AI Agents that deliver on these hopes?
AI Agent Design Framework
I'm developing an approach for how AI builders can approach Agentic AI design strategically. This is based on my ongoing experience in [MindStudio.ai](http://mindstudio.ai/)'s AI Agent Builder Bootcamp, now underway with Cohort 2. It's also based on my years of experience with strategic user experience and product discovery. What I've seen is that successful software products usually have structured thinking about purpose, audience, constraints, and outcomes. What we need for AI agents is a similar framework to maximize our chances for a successful agent.
Who Can Benefit
This advice is for AI professionals, Product Managers, UX Designers, or anyone who is creating AI Agents.
A life cycle point of view helps us plan for the big job picture.
A useful structure for AI Agent design should help builders account for the complete business environment the AI Agent is designed for. Thinking in terms of an AI agent life cycle pushes you to think holistically. When you understand the big picture environment that your AI agent will work in, you can find the best configurations and treadeoffs. You can also account for problems and edge cases. A life cycle model helps us understand all of these factors. This raises our chances of developing AI Agents that are useful, usable, and delightful to use.
AI Agents are workers whose employment history we create in advance
To put it another way, think of AI Agents as workers that we intend to hire. The twist is that with AI Agents, we can plan the whole story of their work history in advance. As with any story, our AI agent's tour of duty will have a beginning, a middle, and an end. We can use this story to plan in advance for effective training, launch, and usage. Everyone affected by the AI agent will know what the
The AI Agent Life Cycle
As part of your AI Agent design and development strategy, think about each of the following stages. Determine what you need to know and do at each stage. Define what the inputs and outputs are for each, and how each connects to its following stage. Audit what you know and don't know. Make a plan to get what's missing.
Discovery
What is the problem you're trying to solve? For whom? What are the business "stakes"? Discover your MSCW ("Moscow"): Must-haves, should-haves, could-haves, won't-haves.
Design
Solve the problem with workflows & screens/voice/etc.. This is the interaction design, the graphic design, and the content that people will see when using the Agent. Also design how the agent will interface with other systems to read and write data.
Build
Create your agent. Cover the main workflows but test for the edge cases. Prototype it with real users and real data until it's solid.
Launch
Consider a beta launch or a pilot program rollout rather than all at once. Update if needed after the pilot. Treat the launch like a film: Previews, internal press release, full demos/walk-throughs, training, owner, a support plan.
Maintain
Update your agent as its components or services change: New or updated API calls and service connections. The AI model that the agent uses itself may be reviewed and changed. You may need to update based on policy or legal changes.
Upgrade/Sunset
Over time, you may add more features and workflows to your agent to add more value: Adding some should-haves and could-haves, maybe a new must-have. A roadmap is handy for planning and communication. It's also likely that the situation will change so dramatically that a new agent using new systems is the answer. In that case, plan for keeping the old agent running while the replacement agent gets a thorough test drive. Broadcast a schedule and plan for how the old agent will be deactivated.
What are your thoughts on how we can approach AI Agent design for maximum effectiveness and success?
Comments, suggestions and criticisms welcome.
Follow me for Part 2, where we look at the functional and topic aspects of AI Agent design.
AI Will Change Us as Knowledge Workers
AI is going to change our economy and businesses in fundamental ways. CEOs and Boards of Directors everywhere seem to be impatient already for this efficiency and profitability revolution. But this revolution in the economy will not happen until we undergo a revolution in our own thinking.
Rapid AI transformation? Not so fast.
There is an interim phase in this AI transformation that will take some time. This step is a personal, human change in ourselves...in our understanding of ourselves as productive workers, thinkers, and do-ers.
What is dawning on us, I believe, is the realization that our psychological habits of thought, possibly even our identity, our sense of self, at least as a professional person, must change because of AI.
Like all human psychological changes, this will take time.
We need AI-savvy knowledge workers
To reshape business with AI, we have to have AI-enabled workers. To be an AI-enabled worker, we have to get clear--or at least comfortable--with a post-AI sense of self, our sense of being a capable worker, of being a valuable person in the working world. That's a process we are all just starting.
We used to use a pencil & a notebook to assist our thinking & planning. Now we will use AI agents. A huge amount of what we call "cognition" is going to move from inside us. It's going to become embodied cognition, that is, it's going to exist in our AI tools, just like we moved some of our thinking to jottings in a notebook.
Giving up work to AI agents
What "thinking" moves to the AI will be the pattern-recognition tasks, because that is what AI really is: A pattern-recognition and language predicting engine. We used to think of this kind of cognition as pretty complicated stuff: summarize this report; write a sales email; generate a project plan; analyze this survey data. But it turns out this knowledge work is really just manipulating or generating structured information according to familiar, predictable patterns. Because AI is so good at creating this type of content, we'll switch to using AI tools to do this kind of "thinking" for us. It can do it faster than we can, with increasing reliability and accuracy. AI tools still unwittingly insert hallucinations--that is, falsities, made-up facts...what look to us like lies.
AI agents do work but they don't think
I say "un-witting" in its root meaning: not thinking, without mind. AI is not understanding anything. It's simply producing language or images that match patterns it has been trained to match. There is no moral or cognitive impulse to tell the truth or to lie. It's a computer program executing to produce an output according to rules. Its output looks and sounds to us as if there is a consciousness behind it. But that is our human hallucination. This is the challenge that knowledge workers of the near future will have to plan for, manage, and overcome. This will be the nature of work and knowledge work going forward.
Our value as knowledge workers will anchor in rare and subtle skills
Our human work will become that of the creative director, systems engineer, project manager, thought leader, subject matter expert, editor, coordinator, evaluator, systems thinker, and curator. We must become leaders of swarms of AI agents that operate according to our instructions. We must be knowledgeable and expert enough to spot hallucinations in the AI work product in time to correct them. Above all, we must become wise enough, creative enough, independent enough, confident enough, and critical enough to create a vision of what we want, and to guide, challenge and correct our AI workers until we achieve that vision. Even if it is just a sales email.
These are higher order creative and cognitive skills. These are leadership skills. These are matters of vision, taste, aesthetics, judgement, discernment, trust, integrity, and morality. Today, only the leaders in our society and economy get to practice these skills to the point of mastery. We define leaders as people who are excellent at these meta-tasks.
The challenge and opportunity of the AI age is for every knowledge worker to shift their identity to center on these skills and their confident expression.
AI will force this change on us. But the AI revolution will not happen until we embrace this change in ourselves.
Note: Orginally published in my LinkedIn Newsletter, Agents and Audiences.
The Stargate Project Hot Takes
1. $500B is a huge amount of money. Likely to be closer to $750B (Micro$oft) as the project progresses. Huge boost for energy and construction sectors.
2. Super Winner: OpenAI and Sam Altman. An extraordinary, game changing deal. Sam has the goods. Securing massive compute & operational control & industry leadership.
3. Winner: NVIDIA. Much of the $500B will go towards securing guaranteed delivery of (all?) their most advanced AI chips for years to come. Time to raise prices.
4. Don't call it a comeback: Oracle. An also-ran in the cloud computing biz just a few years ago, this puts Oracle in the front ranks, in a new light.
5. Losers: Google, Amazon, X. Without similar aggressive capital investment plans, investors and customers may wonder if they can secure the energy, real estate & compute. Google & Amazon could partner to raise $1T. X/Musk could potentially plan on moving Grok data centers into orbit to secure solar power...with someone's $1T investment.
6. Where is Apple?
7. How does Stargate plan to make money? Presumably a % on computation fees.
Post-Covid Speed Run
Our lives have been accelerating and splintering for a while now. Digital, then Internet, then Social, then Mobile. But COVID-19 was disruption of a different nature.
Is it just me, or are people driving like maniacs these days? And don't people seem more binary...ping-ponging between zoned out and acting out? Less courtesy. Less care for our social rituals and spaces. More than usual. More than before.
No Going Back to the Beforetime
Covid response measures are in the rear-view mirror it seems. But their effects remain. We didn't go full Mad Max, but a lot was wiped out.
Millions of small businesses and solo operators went out of business. Millions of people dropped the work commute forever, and commercial real estate worldwide now has no function. Huge cohorts of schoolkids missed developmental milestones, and remain behind in basic skills.
I interviewed a Covid-era college grad recently. She paid full fare with student loans but had to educate herself for her last 2 years, Zoom notwithstanding. She learned she didn't need the institution. It was a just a gatekeeper with a steep toll. How will her generation view the workplace and government?
What Happened with the Response
In a way, the Apocalypse happened. All at once, everyone lost control over basic life: Living together as a family (or attending a dying loved one), socializing with friends, going to work and earning money, sending the kids to school. Public authorities seemed united in scaring the living hell out of everyone, and they succeeded.
I managed to make it to my hometown during the pandemic. The local Spectrum News ran nothing but COVID stories for months. It was relentless. Every single story was about COVID or had a COVID angle, even the weather. Every story was about helplessness, fear, and death. After just a few moments, I had to turn it off to stay sane.
It Was Like AIDS
A lot of people didn't survive the AIDS epidemic in the 80s and 90s. For those of us who did, COVID-haunted daily life brought back unwelcome memories. Death was invisible and everywhere. Inescapable and incurable. You felt a low grade terror that rapid extinction could spring from your next human interaction. COVID fear felt like AIDS fear all over again, except this time for everyone.
Not Back to Normal
We’re still freaked out to realize we have no control over our lives. Bedrock social structures and family routines can be taken away in an instant. Do you know why so many are still on edge? Because we can't escape the sense that it could happen again. Two weeks turned into two years easily. Covid round XXXIII, an ultra flu strain, environmental disaster...could the lockdown hammer fall again?
A Thunderdome World
People are still in fight-or-flight mode, so nobody else matters. It's not a moral failure. What's the point when the present and the future are uncertain? Traumatized people and institutions don't plan for the future. It's about staying alive in the arena today.
So no, there is no time for the old courtesies. There is little time or energy to think about consequences.
I'd like to think we're rebooting...starting a new game with fresh supplies and Max Health.
But it doesn't feel that way. We’re all trapped in a speed run through life now, still on red alert, mouths on full blast.
The WGA Strike is Over...But Is Hollywood Facing a Greater Danger?
With the resolution of the Writers Guild of America strike, and the expected end of the SAG-AFTRA actors union strike shortly, it seems it's back to the business of making shows for Hollywood.
But as former Amazon Head of Film and longtime Indie Film superproducer Ted Hope notes in his recent essay, "Won't Get Fooled Again," Hollywood is suffering from a dangerously limited perspective.
https://open.substack.com/pub/tedhope/p/they-wont-get-fooled-again-or-will
Why is Ted Hope creating such a stir?
Because he throws a spotlight on the 800 pound gorilla in the room that we could sort of smell, but not really see. And the gorilla turns out to be wearing a suit and he is actually the new Boss of Everyone.
The gorilla is Big Tech, of course, and its backers Venture Capital and Wall Street. They have taken over Hollywood without a shot, without anyone really noticing.
The new framework is: Shows are 100% an expense. Not an investment. Content is a resource that must be obtained as cheaply as possible, as they are working against hard ceilings of total addressable market and maximum monthly fees, which limits profit, their only goal. That’s how Wall Street operates and I don’t have a problem with that.
But Hollywood is not just a business like any other that happens to make shows. It is a culture-making business. It cannot function without being art in some form. Even now, the Academy does not honor the highest-grossing films; it recognizes the “best” films. The best films affect the lives and spirits of billions of people for the better, and help us understand ourselves. They also become landmarks in our lives and the life of our culture. This is an intangibile quality that nevertheless has global impact. And a value that is almost too big to be calculated.
Big Tech substituted a commodity model for Hollywood without a discussion because it looked like consumer choice. But this approach may lead Hollywood down the wrong path. What's cheap but holds eyeballs? Shock, lust, envy & outrage programming on a reality tv model. Hello, naked men TV dating show.
Hollywood lost the knowledge of itself as an industry apart, as a culture business that makes money because it creates culture that people enjoy. The big media corporations that own the major studios are not excused from their fiduciary duty to stand up for Hollywood itself...to protect the way the industry makes money and perpetuates itself. They are not doing that.
The gorilla is backstage, and all we get is thumps, and screams, and things moving behind the curtains. Ted Hope's essay is one of the few to ask, “Hey, what’s going on back there?”
To put it another way: As a business, Hollywood projects need to be break-even to moderately to massively profitable. This allows the whole show to continue in a steady state kind of way. Along the way, talented people and smart studios can make a fortune. There can even be growth in the size of the industry itself (although Hollywood needs to get more bold in this area).
On the other hand, as a business, Tech projects must show huge and rapid growth continuously: double-digit gains, quarter over quarter, until competitors are eliminated and markets are captured, and investors can cash out and/or rely on constantly rising profits and dividends. But most of all, size and market capitalization.
In Tech and Finance, no growth is failure. Slow growth is failure. Anything but the hockey stick is failure.
The people running Hollywood today are financial leaders. They want the hockey stick. They want Team Hollywood all suited up and ready to play for the money.
But they can’t see that hiding under their team’s uniform and helmet is the Big Tech Gorilla.
They can’t see body check that’s coming.
The Gazelles and the Lion
A parable of late Web 2.0 social media, inspired by Jaron Lanier
Has Big Tech escaped meaningful scrutiny and consequences because they are already so powerful...the biggest lions whom everyone fears?
Or is it the case that they are the swift gazelles of the economy, now grown bigger than the dinosaurs of the previous age? They seem to be the dominant new herd by right of outpacing all others. So, a boundless new land is theirs to claim. It seems that they can, for now, race far ahead of the plodding old lion of society.
Yet their scent remains in the air. A muzzle lifts. Their intention is known. Could it be that like a squadron of careless aviators who fly into a box canyon, the swift herd of Big Tech sees only a horizon without obstacles? They trample smaller creatures and scare up clouds of fleeing birds. Do they run in a mistaken panic for survival—is that why they pay no mind to the tightening hills?
The deadly tragedy of the box canyon is the wall suddenly towering ahead. There is no room to turn. There is no time to climb. It is inescapable, save for earlier, wiser decisions. The day may yet come when Big Tech meets its wall, and there is nothing left for it to do but wait as slow, approaching footfalls herald a reckoning.
A Short Essay on Short Emails
Waaay back at the beginning of my career, I thought it was very important to explain myself fully in emails.
Explain the situation, the background, the options, the pros and cons, what to do next.
My very favorite thing to do was to point out what is wrong, and why it's wrong, and how to fix it. I was sure all my lucky recipients appreciated my timely thoughts and helpful suggestions.
I didn't just write emails, I wrote reports. I wrote the whole story as I saw it and poured out my views. I was therapizing my out of control ego. I had very patient friends and colleagues.
Of course I finally came to realize that my emails weren't having the effect I hoped. They were going straight into the trash. Today we have an acronym for it: TLDR, for "too long, didn't read."
Today, we get more emails than ever. And I've completely changed my approach to them.
The best emails today are short and sweet, terse and telegraphic, brief and all-business.
Since nine out of ten emails are really requests for information, a to-do being assigned, or a delivery of information, it's best to start crafting your emails to make their purpose drop-dead obvious. Being short is the best thing you can possibly be in an email.
Pro Tips for Awesome Emails
One topic or item per email. No "compound emails." Got multiple, separate things to communicate? Send each in a separate email.
The very best email is just a short, substantive subject line followed by (eom) "end of message." The recipient does not even need to open the email.
If you're working on a named project with others, consider using that project's name as a prefix to your email subject line. For example, "Q4 All Hands Zoom: Link & Agenda" or "Omega project: Meeting notes: September 30, 2021"
Consider adding (fyi) "for your information" as a prefix to the subject line if you're delivering information that the recipient can read later
Three line emails only...yes, that's right, limit yourself to three lines of text in your email (excluding your salutation and your close).
Be really clear and direct. There should be no confusion about what you're saying or asking for
Communicate Better with One Pagers
A lot of "work" consists of large volumes of low information writing
At work, we're reading and writing all day—texts, emails (often with long chains), messages, channel posts, chats. We constantly monitor multiple streams of short, transactional, conversational messages, emails, and posts.
This is a huge amount of communication. Yet it has a low information density.
We're also talking on the phone, and going to meetings, where we talk some more. Sometimes, we get to read presentations. Really long presentations with lots of preamble, discussion, tables and graphs, and appendices. Sometimes there is a clear point.
This means we have to wade through a lot of texting and talking to chip away at our problem. That problem is trying to get a sufficient amount of the right information to do one of three things:
1. Define a business thing, problem, or opportunity
2. Decide to do the thing, and prioritize it against other efforts, or not do the thing
3. Understand what happened with a thing—Get a report of outcomes, progress, results, findings, and what to do about it
Low information density in our communications makes it difficult and time consuming to gain full understanding and then decide with confidence.
Break through the noise with a One Pager strategy
Win friends and influence people by adding a new tool to your communication strategy: The one pager. It's a single sheet—real or digital, but real is often better—that contains:
A clear, concise definition of the topic or proposal, or reporting of findings
A distillation and aggregation of all and only the relevant supporting information in an easy-to-scan format
Pros & cons
Costs & benefits, plus CODN (cost of doing nothing)
Implementation: Ease or difficulty, with remedies to possible problems
Dependencies, impact on others
Pro Tip: Take a stance in your one pager that will force the readers to react. Use the one pager to declare what you intend to do unless you hear otherwise. If there are choices, identify your recommended option and why. If there are priorities, stack rank them and identify which ones are for now and which ones will be backlogged. If there are multiple next steps, state the one you will take immediately. If you need action from others, make your ask.
Communication strategy with one pagers:
Send out 24-48 hours ahead of time
Meet
Present and/or distribute your one pager
Discuss
Take note of any adjustments, changes, and most importantly, the decision
Issue a revised one pager with the decision stated explicitly
One pagers are approachable and easy to deal with, yet their high information density cuts through the usual fog of business. They save time. They promote focused discussion. They clarify decision making. They promote consensus and action.
Notebooks Make You Smarter
It started innocently enough.
In my earlier career, working on UX projects for Fortune 500 companies, I needed a way to keep track of all the insights, facts, diagrams, players, requirements, and plain old to-do's.
So I returned to a habit I thought I had left behind: Taking notes in a physical notebook. Now, 20-mumble years on, I am never without a notebook. I take notes on everything: Books, podcasts, meetups, conferences, Twitter threads, everything. And I have a whole system of physical notebooks with handwritten notes.
Why?
The research is long in: Handwritten notes make your brain remember concepts better. When you activate the hand-brain connection to write, you process information more deeply. You can condense, restate, annotate, illustrate, and cross-reference other entries. Most importantly, you will remember information better. When you talk, you'll have more substantive, better structured things to say from memory.
My current notebook lineup includes:
Morning pages notebook
Daily mind dump...automatic writing in a big, cheap spiral bound notebook like you had in the eighth grade. Get your swirling thoughts down on paper so focused thinking can begin.
UX work notebook
Big, sturdy Rhodias. Always at hand. Every note & to-do goes here. With these handwritten records, I can reconstruct the memory of a meeting, utterance, or decision going back months (if you choose to keep them). 1st IA, flow diagrams, and wireframes happen here. https://rhodiapads.com/collections_spiral_4colorbook.php
Professional/Industry notebook
Small, softback Leuchtturm handbooks (fit in any bag or coat pocket) for professional training notes, meetups, conferences, speaker notes, business book notes and especially deconstructed podcasts. Indexed and saved when full. https://www.leuchtturm1917.us/notebook-classic.html
Personal journal and commonplace book
Small, hardback Leuchtturm notebooks that house my own journal entries. It's also a commonplace book: A copied record of anything good from your reading, watching or anyplace else. Memorable quotes, pithy tweets, excerpts from fiction and non-fiction, recipes, whole poems. Indexed and saved when full. Writer Ryan Holliday says more: https://ryanholiday.net/how-and-why-to-keep-a-commonplace-book/
Pro Tip: Make an Index for Your Notebook
Save 4-5 blank pages at the front of each notebook. Label them "Index"
Number the pages of your notebook. Or buy pre-numbered notebooks
When your notebook is full, review the pages for what's good. These will go in your index
Give each important topic or concept a descriptive line on an Index page. Add the page number or range
If the topic appears in multiple places in your notes, keep the single line item for it in the index. Add additional page numbers or page ranges next to it
Later, to find a note, you only need to scan the index, not flip through all the pages
Morning Pages: Clear Your Mind, Fight Dopamine Addiction
What if there was a way you could clear your mind, strengthen your ability to show up, and also push back on your smartphone social media dopamine-hit-addicting doomscrolling?
There's a way that's working for me, and it might work for you, too.
It's called Morning Pages. It's a writing practice launched by Julia Cameron in her book, "The Artist's Way." https://juliacameronlive.com/basic-tools/morning-pages/
Morning pages are a stream-of-consciousness writing practice meant to clear the mind and get it ready to do real writing work. Or get ready for whatever your work is. It's throwaway writing, like sweeping your mind of the night's detritus.
And it's just 3 longhand pages a day, every morning. Write anything and everything that comes to your mind. Your dreams, your worries, your fears, and even silly, useless thoughts. Just get them out of your head and onto paper.
Sometimes a good idea pops up in your morning writing routine. You'd be surprised. Copy it over to another notebook or to your digital notes.
"There is no wrong way to do Morning Pages—they are not high art. They are not even 'writing.'” —Julia Cameron
Added bonus: If you sit down to Morning Pages first thing every day, then you're training yourself not to pick up your phone. That's less doomscrolling social media, checking email, scanning Slack channels, and the million other little time wasters that give us that morning dopamine hit we are all hooked on. Doing Morning Pages weans you from your morning phone addiction.
How to Do Morning Pages
1. Buy a fat, cheap composition notebook. The cheaper the better. You're not actually going to keep this notebook—You'll toss it, shred it, burn it when it's full.
2. Get some pens or pencils
3. Find a morning writing spot and put your notebook and pen there.
4. At night, hide your phone away or turn it off. Keep it out of your writing spot.
5. First thing in the morning (with a stop for coffee), sit down and start writing without thinking. Just keep your pen moving.
6. Stop when you hit 3 pages. Some days, my flow stops at 1 or 2 pages. That's OK.
7. Repeat every day for a few weeks.
8. Start enjoying a calmer mind and a cooler relationship to your phone
Writing My Way Forward
Social feeds stuffed with videos, streaming television and films, an explosion of digital gaming of all types...this is our Internet now. This has led many pundits to say that the age of the written word is over. They say we are becoming a visual image and spoken word culture once again...modern primitives, in a way.
But here and there, in corners of our digital culture, writing seems to be making a comeback. Or more precisely, writing seems to be appreciated by more and more people for the focus and weight it can offer to our distracted minds and noisy world. These people are the real knowledge workers of today: People who seek to clarify their thinking on topics that matter to them. These thoughts can also be put into the world to help others and influence things for the better. Thoughts that are honed through writing seem to survive better in our chaotic digital culture. We want to find like-minded friends, allies, contacts, and employers. We may even want to find ourselves. For all of these goals, there is no substitute for writing.
Recently, I've been browsing and trying a variety of writing techniques. So far, the ones that have stuck are morning pages and making reading notes in journals. But both of these are background activities. Something is missing.
The missing piece is becoming a daily writer. To that end, I've joined up with the Ship 30 for 30 folks. My fellow writers and I will be sending our Atomic Essays into the world, one a day for the next 30 days.
On my left is the pile of my experience of life. I am knee-deep. I must process it, compost stuff, maybe even uncover something long buried.
On my right is the way forward, but there is no path.
Writing, I will make bricks, one by one. I'll uncover the good stuff in the pile. And brick by brick, I will build a path, even though I don't know where it may lead.