Agentsable
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Live interactive training for Product Managers, Senior PMs & Technical PMs

Build and control real AI agents in plain text.

We built the first interactive training designed to let PMs experience AI agents firsthand. Our students see live AI agents in action in the first 15 minutes, then design and build their own agent systems using Raw Agent Control (RAC). You define agents, workflows, and tools in plain text without changing code or using an agentic framework.

Run live agents.Master Raw Agent Control (RAC).Become Agentsable.

Join the Interactive Training

Watch how editing a plain text RAC file steers live multi agent behavior in real time (zero coding required).

How it works · The 3 pillars

From firsthand agent action to building any AI system you can imagine.

01

Run live agents.

In AI agents, the fine details matter more than the code, and PMs own the details.

Programmers focus on code syntax, while PMs know the user context, edge cases, and business boundaries. Because an agent's behavior comes down to its responsibilities, instructions, and guardrails, and because real agents hallucinate and loop when data gets messy, you cannot learn this from slide decks. In our interactive training, you run live agents in the first 15 minutes, watching how they think, fail, and respond to your plain text adjustments in real time.

Three fuzzy agent spheres in coral, blue and purple orbiting a glowing chat bubble
02

Master Raw Agent Control (RAC).

RAC is a plain text design system for complex agentic workflows, not another coding framework.

Just like Markdown defines the design of a document in simple text that can be rendered anywhere, RAC lets you define agents, tools, guardrails, and pass or repeat gates in structured text files. You test and refine the workflow live, and then hand those exact definition files to your engineers or AI coding tools like Cursor and Claude Code as a verified architectural specification contract ready for your existing codebase. No visual black boxes, zero framework lock in, and full compatibility with your current codebase.

Three labelled agent spheres circling a translucent AGENTS.md rule file
03

Become Agentsable.

Turn every idea in your head into a real, working AI system.

You chose product management because you wanted to build the future, not just coordinate sprints. When you master Raw Agent Control, the gap between having a breakthrough idea and executing it disappears. You will leave this interactive training having built, debugged, and refined a production ready agentic system from scratch, walking away with the permanent superpower to build, fix, and scale any AI system you can imagine.

A branching agent workflow of purple, blue and coral nodes flowing left to right

Weekly format · Built for busy PMs

Designed for busy Product Managers. Even if you miss the recorded videos, you never fall behind.

Every week follows a four step interactive cycle built to take you from concept to hands on plain text execution in a single sitting:

  1. 01

    On demand lectures

    Recorded videos unlocked weekly.

    Short, focused video breakdowns released after each weekly session so you can explore the core concepts at your own pace.

  2. 02

    Weekly live Q&A

    Fast concept recap & live Q&A on Zoom.

    We kick off each live session with a clear review of the recorded material and answer your specific questions.

  3. 03

    Live RAC build session

    Watch a live agent system built in plain text.

    We build a complete, working example live on screen using the RAC system. Even if you missed the recorded videos, the Q&A and Live RAC Build give you 100% of what you need.

  4. 04

    Hands on browser lab

    Take control by editing plain text.

    Immediately after the live build, you get a ready to run version of that exact system in your browser lab and implement it yourself purely by editing the plain text files and testing execution, with zero local installations or IT permissions required.

The syllabus · 4 weeks of live control

Four weeks from fragile prompts to production ready agentic workflows.

  1. Week 01 · Agent brains

    From Prompt to Agent: Defining a Structured Plain Text Agent.

    Move from fragile chat prompts to structured plain text definitions, mapping Role, Context, Decision Logic, and Output Contracts.

    Live lab challenge

    Take over the agent from the live build that is misclassifying edge cases. Fix its reasoning behavior by refining its plain text RAC rules.

  2. Week 02 · Tools & memory

    The RAC Agent Tools, MCP & Memory Definition.

    Define external tools, Model Context Protocol (MCP) connections, memory rules, and multi agent handoffs in readable plain text.

    Live lab challenge

    Take the multi agent workflow from the live build and wire its tools, memory rules, and agent to agent handoffs in plain text so the team completes a multi step task end to end.

  3. Week 03 · Failure patterns

    When Agents Fail: Fail Patterns of Agentic Workflows.

    Diagnose live execution traces and fix the 4 real world failures: infinite tool loops, context decay, broken data formats, and cascading multi agent errors.

    Live lab challenge

    Inspect a broken multi agent workflow trace from the live build. Diagnose the exact failure pattern and stop the loop by adjusting the plain text definitions.

  4. Week 04 · Production control

    Guardrails, LLM as a Judge & Human in the Loop Systems.

    Lock in hard operational boundaries, automated pass or repeat evaluator gates, human in the loop escalation triggers, and a verified specification contract ready for your engineering team.

    Live lab challenge

    Add plain text guardrails, an LLM as a Judge pass or repeat gate, and a Human in the Loop escalation rule to the live build example. Practise the controls needed for production readiness.

Explore the complete syllabus

Why agents fail · Real world reliability

Most AI courses only show you what happens when things work. We train you for when they break.

Anyone can build an agent that looks like magic on Friday in front of a VP, only to watch it hit messy customer data on Monday morning, loop in circles, and burn through thousands of dollars in API tokens. In our interactive training, we intentionally trigger real world breakdowns inside your browser lab so you learn how to diagnose and fix the 4 failure patterns that ruin production AI systems, purely through plain text RAC definitions:

  1. 01 · Fail pattern: Infinite loops

    The agent goes in circles.

    When an agent receives ambiguous data or an unexpected tool response, it repeats the exact same tool call endlessly until your token budget burns out.

    The Plain Text RAC Fix: Define strict iteration caps, retry limits, and fallback instructions outside the prompt.

  2. 02 · Fail pattern: Context decay

    The agent forgets its own rules mid task.

    As multi step workflows grow longer, bloated memory causes attention degradation, making the agent drop critical instructions and ignore business boundaries.

    The Plain Text RAC Fix: Structure memory rules to control exactly what context is kept or pruned at every step.

  3. 03 · Fail pattern: Silent hallucinations & broken formats

    The agent invents fake data to move forward.

    Instead of flagging missing information, a probabilistic agent will silently hallucinate parameters or break the output format expected by the next step.

    The Plain Text RAC Fix: Lock in strict output contracts and automated LLM as a Judge pass or repeat gates.

  4. 04 · Fail pattern: Cascading errors

    One bad step poisons the entire workflow.

    In a multi agent workflow, a small mistake by the first agent gets treated as verified truth by downstream agents, snowballing into a completely broken outcome.

    The Plain Text RAC Fix: Wire verification handoffs and Human in the Loop escalation gates before high stakes actions.

The transformation · Before & after

Master the AI wave. Become Agentsable.

Same sharp product instincts, now backed by the power to build, debug, and ship real AI agent systems in plain text.

  1. Before

    You rely on blind no code boxes or vibe coded demos that engineers dismiss in architecture reviews

    After

    You run live agents in a browser lab and see every step of how they think and act

  2. Before

    You wait two full sprints just for engineering to tweak a system prompt or agent retry rule

    After

    You define agents, tools, MCP, and memory yourself in clean plain text RAC files

  3. Before

    You get stuck manually triaging AI slop or worrying an unmonitored agent will hallucinate in production

    After

    You inspect live traces, spot the failure pattern, and lock in LLM as a Judge and Human in the Loop gates

  4. Before

    Locked into a single vendor tool or proprietary coding framework

    After

    Verified plain text specification contracts that your engineers, Cursor, and Claude Code implement in any codebase without pushback

Enrollment details · Start building

Join the four week interactive training, live and hands on.

Run your first live agent in minute 15, master Raw Agent Control (RAC) in plain text, and walk away ready to build any AI system you can imagine. We stripped out thousands of dollars of passive slide lectures to deliver 100% live execution inside an automated browser lab, priced to fit directly inside your auto approved corporate learning stipend.

Reserve Your Seat & Enter the RAC Lab See the syllabus

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