01Start here
Research & Write Loop
A complete multi-step agent loop that fetches data from external sources, processes and summarises it, and writes structured output — fully automated and ready to adapt to your domain.
See the session planYour AI agent shouldn't need you to babysit it. In one intensive session you'll build loops that run, retry, checkpoint, and hand off to humans at exactly the right moment — and ship them the same day.
Your take-home work
Build these with your instructor. Keep them, adapt them, and put them to work after the workshop.
01Start here
A complete multi-step agent loop that fetches data from external sources, processes and summarises it, and writes structured output — fully automated and ready to adapt to your domain.
See the session plan02Make it yours
A content workflow loop with approval gates at the right moments — autonomous drafting, human review before publish, automated distribution after sign-off.
What you’ll take away
Intermediate-level workshop
Loops are the backbone of every real AI agent. Not the flashy demo — the part that actually runs, retries, monitors itself, and knows when to hand back to a human. This workshop strips back the hype and teaches you the engineering fundamentals: how to structure a loop, where to put checkpoints, how to handle failure, and when autonomous is the right call vs. when human approval is non-negotiable. By the end you'll have three working loop templates — a multi-step research runner, a human-gated content pipeline, and a failure-resilient task runner — all built during the session and ready to drop into a real project.
The curriculum
4 hours live · Recordings included
Dissect what a loop actually is in an agent context: the trigger, the task queue, state management between iterations, and the termination condition. Understand the difference between a loop that runs once and one that keeps going until the job is done. Build your first simple task-runner loop in Claude Code end-to-end.
skills
Everything you practise hands-on across the live sessions.
Founder of AISOLO Technologies; AI entrepreneur & educator
350K+
Students
12+
Yrs exp
3
Startups
Yash Thakker is the founder of AISOLO Technologies and one of the most sought-after AI educators in the country. He's known for one thing above all: by the end of any session, students have already built something — no saving it for 'later.' His classes are built around real-life, happening-right-now examples, zero dry theory, and a teaching energy that makes complex things feel obvious. With over a decade shipping AI products across media, fintech, and edtech, Yash has delivered every learning format imaginable — in-person workshops, live bootcamps, online courses, and hybrid sessions — always exploring new ways to make learning land faster. He's obsessed with what learning looks like for this generation, now that AI changes what's even worth teaching. He has reached 350,000+ learners and is the creator behind Olly.social, BGBlur.com, Infloq.com, and explainx.ai.
Your next step
Jul 20, 2026 · 1 days · 4 hrs/day
1 live session
4 hours · Jul 20, 2026
Session recording
1-year access to the full recording
Loop pattern library
Copy-paste templates for the most common agent loop shapes
Human-in-the-loop toolkit
Checkpoint and approval gate templates ready to integrate
Private Discord channel
Community access for Q&A and discussion
Melo learning assistant
Pre learning, all workshop content, and post learning practice
Certificate
Verified completion certificate from explainx.ai
This run sold out. Open workshops still have seats — grab one before they fill.
Notify me about the next cohortAnnual includes this plus 5× more Melo usage than Free — every workshop, $300/yr
faq
Everything you need to know before enrolling.
You should be comfortable writing prompts and have basic familiarity with Claude Code or a similar AI coding assistant. No prior experience with agent frameworks or loop patterns required.
Complete both sessions and receive a verified certificate from explainx.ai — add it to your LinkedIn, portfolio, or resume.
Sample only · Each issued certificate has a public verification UUID
View sample certificate ↗
Built around real workPrivate team sessions
Bring Loop Engineering: Build Agents That Run Themselves to your company with examples, projects, and guidance shaped around the tools your team already uses.
Real messages from students who trained with Yash across his live AI workshops and bootcamps.
“I'd built loops before but they were brittle — one failed API call and the whole thing died. After this session I understand retry logic and failure isolation properly. My loops actually stay running now.”
“The human-in-the-loop session was the thing I didn't know I needed. I was either running everything autonomously (scary) or approving every step manually (pointless). Now I know exactly where to put the gate.”
“Four hours felt short but we covered a huge amount of ground. I came in writing one-shot prompts and left with a proper multi-step research loop running in production. The pace was intense in the best way.”
“Yash builds everything live on screen — no pre-recorded snippets. When something breaks he debugs it in real time and that's honestly where the most learning happens. I'll be back for the next one.”
03Make it yours
A production-grade loop template with per-step error capture, retry logic, and structured logging — drop it into any workflow that needs to keep running even when individual steps break.
Wire real tools into your loop — file reads, API calls, code execution. Add conditional branching so the loop can change course based on intermediate results. Build a multi-step research-and-write loop that fetches data, processes it, and produces a structured output without manual intervention.
Not every step should run autonomously. Learn the two checkpoint patterns: blocking approvals (loop pauses and waits) and async approvals (loop queues work and resumes). Design approval gates for the moments that matter — destructive writes, external sends, budget thresholds — and keep autonomous everything that doesn't need a human.
Loops fail. Learn structured failure handling — per-step error capture, exponential backoff retries, and hard-stop conditions. Understand loop observability basics: what to log, when to alert, and how to replay a failed run without duplicating side effects. Close with a review of the loop patterns that cover 90% of real production use cases.
Skills you’ll practice: Loop anatomy and state management · Tool call integration · Conditional branching · Human-in-the-loop patterns · Approval gates · Failure handling and retries · Loop observability
Booked
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