That is the primary article in a collection on agentic engineering and AI-driven growth. Search for the following article on March 19 on O’Reilly Radar.
There’s been quite a lot of hype about AI and software program growth, and it is available in two flavors. One says, “We’re all doomed, that instruments like Claude Code will make software program engineering out of date inside a yr.” The opposite says, “Don’t fear, all the things’s nice, AI is simply one other software within the toolbox.” Neither is sincere.
I’ve spent over 20 years writing about software program growth for practitioners, masking all the things from coding and structure to mission administration and group dynamics. For the final two years I’ve been targeted on AI, coaching builders to make use of these instruments successfully, writing about what works and what doesn’t in books, articles, and studies. And I saved working into the identical drawback: I had but to seek out anybody with a coherent reply for the way skilled builders ought to really work with these instruments. There are many ideas and loads of hype however little or no construction, and little or no you could possibly follow, educate, critique, or enhance.
I’d been observing builders at work utilizing AI with numerous ranges of success, and I noticed we have to begin excited about this as its personal self-discipline. Andrej Karpathy, the previous head of AI at Tesla and a founding member of OpenAI, just lately proposed the time period “agentic engineering” for disciplined growth with AI brokers, and others like Addy Osmani are getting on board. Osmani’s framing is that AI brokers deal with implementation however the human owns the structure, opinions each diff, and checks relentlessly. I believe that’s proper.
However I’ve spent quite a lot of the final two years educating builders use instruments like Claude Code, agent mode in Copilot, Cursor, and others, and what I preserve listening to is that they already know they need to be reviewing the AI’s output, sustaining the structure, writing checks, holding documentation present, and staying in charge of the codebase. They know do it in principle. However they get caught attempting to use it in follow: How do you really evaluate hundreds of strains of AI-generated code? How do you retain the structure coherent whenever you’re working throughout a number of AI instruments over weeks? How are you aware when the AI is confidently fallacious? And it’s not simply junior builders who’re having bother with agentic engineering. I’ve talked to senior engineers who battle with the shift to agentic instruments, and intermediate builders who take to it naturally. The distinction isn’t essentially the years of expertise; it’s whether or not they’ve found out an efficient and structured option to work with AI coding instruments. That hole between realizing what builders needs to be doing with agentic engineering and realizing combine it into their day-to-day work is an actual supply of hysteria for lots of engineers proper now. That’s the hole this collection is attempting to fill.
Regardless of what a lot of the hype about agentic engineering is telling you, this sort of growth doesn’t remove the necessity for developer experience; simply the other. Working successfully with AI brokers really raises the bar for what builders have to know. I wrote about that have hole in an earlier O’Reilly Radar piece known as “The Cognitive Shortcut Paradox.” The builders who get essentially the most from working with AI coding instruments are those who already know what good software program appears like, and may usually inform if the AI wrote it.
The concept that AI instruments work finest when skilled builders are driving them matched all the things I’d noticed. It rang true, and I wished to show it in a approach that different builders would perceive: by constructing software program. So I began constructing a particular, sensible strategy to agentic engineering constructed for builders to comply with, after which I put it to the check. I used it to construct a manufacturing system from scratch, with the rule that AI would write all of the code. I wanted a mission that was advanced sufficient to stress-test the strategy, and fascinating sufficient to maintain me engaged by means of the exhausting elements. I wished to use all the things I’d realized and uncover what I nonetheless didn’t know. That’s after I got here again to Monte Carlo simulations.
The experiment
I’ve been obsessive about Monte Carlo simulations ever since I used to be a child. My dad’s an epidemiologist—his entire profession has been about discovering patterns in messy inhabitants knowledge, which implies statistics was all the time a part of our lives (and it additionally signifies that I realized SPSS at a really early age). Once I was perhaps 11 he informed me concerning the drunken sailor drawback: A sailor leaves a bar on a pier, taking a random step towards the water or towards his ship every time. Does he fall in or make it residence? You’ll be able to’t know from any single run. However run the simulation a thousand instances, and the sample emerges from the noise. The person consequence is random; the combination is predictable.
I keep in mind writing that simulation in BASIC on my TRS-80 Coloration Pc 2: just a little blocky sailor stumbling throughout the display, two steps ahead, one step again. The drunken sailor is the “Whats up, world” of Monte Carlo simulations. Monte Carlo is a way for issues you possibly can’t resolve analytically: You simulate them tons of or hundreds of instances and measure the combination outcomes. Every particular person run is random, however the statistics converge on the true reply because the pattern dimension grows. It’s a method we mannequin all the things from nuclear physics to monetary danger to the unfold of illness throughout populations.
What for those who might run that type of simulation in the present day by describing it in plain English? Not a toy demo however hundreds of iterations with seeded randomness for reproducibility, the place the outputs get validated and the outcomes get aggregated into precise statistics you need to use. Or a pipeline the place an LLM generates content material, a second LLM scores it, and something that doesn’t go will get despatched again for one more strive.
The purpose of my experiment was to construct that system, which I known as Octobatch. Proper now, the trade is consistently in search of new real-world end-to-end case research in agentic engineering, and I wished Octobatch to be precisely that case examine.
I took all the things I’d realized from educating and observing builders working with AI, put it to the check by constructing an actual system from scratch, and turned the teachings right into a structured strategy to agentic engineering I’m calling AI-driven growth, or AIDD. That is the primary article in a collection about what agentic engineering appears like in follow, what it calls for from the developer, and how one can apply it to your individual work.
The result’s a completely functioning, well-tested utility that consists of about 21,000 strains of Python throughout a number of dozen recordsdata, backed by full specs, practically a thousand automated checks, and high quality integration and regression check suites. I used Claude Cowork to evaluate all of the AI chats from your entire mission, and it seems that I constructed your entire utility in roughly 75 hours of lively growth time over seven weeks. For comparability, I constructed Octobatch in simply over half the time I spent final yr enjoying Blue Prince.
However this collection isn’t nearly Octobatch. I built-in AI instruments at each stage: Claude and Gemini collaborating on structure, Claude Code writing the implementation, LLMs producing the pipelines that run on the system they helped construct. This collection is about what I realized from that course of: the patterns that labored, the failures that taught me essentially the most, and the orchestration mindset that ties all of it collectively. Every article pulls a distinct lesson from the experiment, from validation structure to multi-LLM coordination to the values that saved the mission on observe.
Agentic engineering and AI-driven growth
When most individuals speak about utilizing AI to put in writing code, they imply one in every of two issues: AI coding assistants like GitHub Copilot, Cursor, or Windsurf, which have advanced effectively past autocomplete into agentic instruments that may run multifile modifying classes and outline customized brokers; or “vibe coding,” the place you describe what you need in pure language and settle for no matter comes again. These coding assistants are genuinely spectacular, and vibe coding could be actually productive.
Utilizing these instruments successfully on an actual mission, nonetheless, sustaining architectural coherence throughout hundreds of strains of AI-generated code, is a distinct drawback fully. AIDD goals to assist resolve that drawback. It’s a structured strategy to agentic engineering the place AI instruments drive substantial parts of the implementation, structure, and even mission administration, when you, the human within the loop, determine what will get constructed and whether or not it’s any good. By “construction,” I imply a set of practices builders can study and comply with, a option to know whether or not the AI’s output is definitely good, and a option to keep on observe throughout the lifetime of a mission. If agentic engineering is the self-discipline, AIDD is one option to follow it.
In AI-driven growth, builders don’t simply settle for ideas or hope the output is right. They assign particular roles to particular instruments: one LLM for structure planning, one other for code execution, a coding agent for implementation, and the human for imaginative and prescient, verification, and the selections that require understanding the entire system.
And the “pushed” half is literal. The AI is writing nearly the entire code. One among my floor guidelines for the Octobatch experiment was that I might let AI write all of it. I’ve excessive code high quality requirements, and a part of the experiment was seeing whether or not AIDD might produce a system that meets them. The human decides what will get constructed, evaluates whether or not it’s proper, and maintains the constraints that preserve the system coherent.
Not everybody agrees on how a lot the developer wants to remain within the loop, and the absolutely autonomous finish of the spectrum is already producing cautionary tales. Nicholas Carlini at Anthropic just lately tasked 16 Claude situations to construct a C compiler in parallel with no human within the loop. After 2,000 classes and $20,000 in API prices, the brokers produced a 100,000-line compiler that may construct a Linux kernel however isn’t a drop-in alternative for something, and when all 16 brokers bought caught on the identical bug, Carlini needed to step again in and partition the work himself. Even robust advocates of a very hands-off, vibe-driven strategy to agentic engineering would possibly name {that a} step too far. The query is how a lot human judgment it’s good to make that code reliable, and what particular practices enable you apply that judgment successfully.
The orchestration mindset
If you wish to get builders excited about agentic engineering in the suitable approach, you must begin with how they consider working with AI, not simply what instruments they use. That’s the place I began after I started constructing a structured strategy, and it’s why I began with habits. I developed a framework for these known as the Sens-AI Framework, printed as each an O’Reilly report (Important Pondering Habits for Coding with AI) and a Radar collection. It’s constructed round 5 practices: offering context, doing analysis earlier than prompting, framing issues exactly, iterating intentionally on outputs, and making use of vital considering to all the things the AI produces. I began there as a result of habits are the way you lock in the way in which you consider the way you’re working. With out them, AI-driven growth produces plausible-looking code that falls aside below scrutiny. With them, it produces techniques {that a} single developer couldn’t construct alone in the identical time-frame.
Habits are the inspiration, however they’re not the entire image. AIDD additionally has practices (concrete strategies like multi-LLM coordination, context file administration, and utilizing one mannequin to validate one other’s output) and values (the ideas behind these practices). For those who’ve labored with Agile methodologies like Scrum or XP, that construction needs to be fairly acquainted: Practices inform you work day-to-day, and habits are the reflexes you develop in order that the practices change into automated.
Values usually appear weirdly theoretical, however they’re an vital piece of the puzzle as a result of they information your choices when the practices don’t provide you with a transparent reply. There’s an rising tradition round agentic engineering proper now, and the values you carry to your mission both match or conflict with that tradition. Understanding the place the values come from is what makes the practices stick. All of that results in a complete new mindset, what I’m calling the orchestration mindset. This collection builds all 4 layers, utilizing Octobatch because the proving floor.
Octobatch was a deliberate experiment in AIDD. I designed the mission as a check case for your entire strategy, to see what a disciplined AI-driven workflow might produce and the place it will break down, and I used it to use and enhance the practices and values to make them efficient and straightforward to undertake. And whether or not by intuition or coincidence, I picked the proper mission for this experiment. Octobatch is a batch orchestrator. It coordinates asynchronous jobs, manages state throughout failures, tracks dependencies between pipeline steps, and makes certain validated outcomes come out the opposite finish. That type of system is enjoyable to design however quite a lot of the main points, like state machines, retry logic, crash restoration, and price accounting, could be tedious to implement. It’s precisely the type of work the place AIDD ought to shine, as a result of the patterns are effectively understood however the implementation is repetitive and error-prone.
Orchestration—the work of coordinating a number of unbiased processes towards a coherent consequence—advanced right into a core concept behind AIDD. I discovered myself orchestrating LLMs the identical approach Octobatch orchestrates batch jobs: assigning roles, managing handoffs, validating outputs, recovering from failures. The system I used to be constructing and the method I used to be utilizing to construct it adopted the identical sample. I didn’t anticipate it after I began, however constructing a system that orchestrates AI seems to be a fairly good option to learn to orchestrate AI. That’s the unintentional a part of the unintentional orchestrator. That parallel runs by means of each article on this collection.
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The trail to batch
I didn’t start the Octobatch mission by beginning with a full end-to-end Monte Carlo simulation. I began the place most individuals begin: typing prompts right into a chat interface. I used to be experimenting with totally different simulation and era concepts to present the mission some construction, and some of them caught. A blackjack technique comparability turned out to be a fantastic check case for a multistep Monte Carlo simulation. NPC dialogue era for a role-playing recreation gave me a inventive workload with subjective high quality to measure. Each had the identical form: a set of structured inputs, every processed the identical approach. So I had Claude write a easy script to automate what I’d been doing by hand, and I used Gemini to double-check the work, make certain Claude actually understood my ask, and repair hallucinations. It labored nice at small scale, however as soon as I began working greater than 100 or so models, I saved hitting fee limits, the caps that suppliers placed on what number of API requests you may make per minute.
That’s what pushed me to LLM batch APIs. As a substitute of sending particular person prompts separately and ready for every response, the main LLM suppliers all supply batch APIs that allow you to submit a file containing all your requests directly. The supplier processes them on their very own schedule; you await outcomes as an alternative of getting them instantly, however you don’t have to fret about fee caps. I used to be joyful to find additionally they value 50% much less, and that’s after I began monitoring token utilization and prices in earnest. However the actual shock was that batch APIs carried out higher than real-time APIs at scale. As soon as pipelines bought previous the 100- or 200-unit mark, batch began working considerably sooner than actual time. The supplier processes the entire batch in parallel on their infrastructure, so that you’re not bottlenecked by round-trip latency or fee caps anymore.
The swap to batch APIs modified how I considered the entire drawback of coordinating LLM API calls at scale, and led to the thought of configurable pipelines. I might chain levels collectively: The output of 1 step might change into the enter to the following, and I might kick off the entire pipeline and are available again to completed outcomes. It seems I wasn’t the one one making the shift to batch APIs. Between April 2024 and July 2025, OpenAI, Anthropic, and Google all launched batch APIs, converging on the identical pricing mannequin: 50% of the real-time fee in trade for asynchronous processing.
You in all probability didn’t discover that each one three main AI suppliers launched batch APIs. The trade dialog was dominated by brokers, software use, MCP, and real-time reasoning. Batch APIs shipped with comparatively little fanfare, however they signify a real shift in how we are able to use LLMs. As a substitute of treating them as conversational companions or one-shot SaaS APIs, we are able to deal with them as processing infrastructure, nearer to a MapReduce job than a chatbot. You give them structured knowledge and a immediate template, they usually course of all of it and hand again the outcomes. What issues is you can now run tens of hundreds of those transformations reliably, at scale, with out managing fee limits or connection failures.
Why orchestration?
If batch APIs are so helpful, why can’t you simply write a for-loop that submits requests and collects outcomes? You’ll be able to, and for easy circumstances a fast script with a for-loop works nice. However when you begin working bigger workloads, the issues begin to pile up. Fixing these issues turned out to be one of the crucial vital classes for growing a structured strategy to agentic engineering.
First, batch jobs are asynchronous. You submit a job, and outcomes come again hours later, so your script wants to trace what was submitted and ballot for completion. In case your script crashes within the center, you lose that state. Second, batch jobs can partially fail. Possibly 97% of your requests succeeded and three% didn’t. Your code wants to determine which 3% failed, extract them, and resubmit simply these objects. Third, for those who’re constructing a multistage pipeline the place the output of 1 step feeds into the following, it’s good to observe dependencies between levels. And fourth, you want value accounting. If you’re working tens of hundreds of requests, you need to know the way a lot you spent, and ideally, how a lot you’re going to spend whenever you first begin the batch. Each one in every of these has a direct parallel to what you’re doing in agentic engineering: holding observe of the work a number of AI brokers are doing directly, coping with code failures and bugs, ensuring your entire mission stays coherent when AI coding instruments are solely wanting on the one half presently in context, and stepping again to have a look at the broader mission administration image.
All of those issues are solvable, however they’re not issues you need to resolve time and again (in each conditions—whenever you’re orchestrating LLM batch jobs or orchestrating AI coding instruments). Fixing these issues within the code gave some fascinating classes concerning the total strategy to agentic engineering. Batch processing strikes the complexity from connection administration to state administration. Actual-time APIs are exhausting due to fee limits and retries. Batch APIs are exhausting as a result of you must observe what’s in flight, what succeeded, what failed, and what’s subsequent.
Earlier than I began growth, I went in search of present instruments that dealt with this mix of issues, as a result of I didn’t need to waste my time reinventing the wheel. I didn’t discover something that did the job I wanted. Workflow orchestrators like Apache Airflow and Dagster handle DAGs and activity dependencies, however they assume duties are deterministic and don’t present LLM-specific options like immediate template rendering, schema-based output validation, or retry logic triggered by semantic high quality checks. LLM frameworks like LangChain and LlamaIndex are designed round real-time inference chains and agent loops—they don’t handle asynchronous batch job lifecycles, persist state throughout course of crashes, or deal with partial failure restoration on the chunk stage. And the batch API consumer libraries from the suppliers themselves deal with submission and retrieval for a single batch, however not multistage pipelines, cross-step validation, or provider-agnostic execution.
Nothing I discovered lined the total lifecycle of multiphase LLM batch workflows, from submission and polling by means of validation, retry, value monitoring, and crash restoration, throughout all three main AI suppliers. That’s what I constructed.
Classes from the experiment
The purpose of this text, as the primary one in my collection on agentic engineering and AI-driven growth, is to put out the speculation and construction of the Octobatch experiment. The remainder of the collection goes deep on the teachings I realized from it: the validation structure, multi-LLM coordination, the practices and values that emerged from the work, and the orchestration mindset that ties all of it collectively. Just a few early classes stand out, as a result of they illustrate what AIDD appears like in follow and why developer expertise issues greater than ever.
- It’s a must to run issues and test the information. Bear in mind the drunken sailor, the “Whats up, world” of Monte Carlo simulations? At one level I observed that after I ran the simulation by means of Octobatch, 77.5% of the sailors fell within the water. The outcomes for a random stroll needs to be 50/50, so clearly one thing was badly fallacious. It turned out the random quantity generator was being re-seeded at each iteration with sequential seed values, which created correlation bias between runs. I didn’t establish the issue instantly; I ran a bunch of checks utilizing Claude Code as a check runner to generate every check, run it, and log the outcomes; Gemini appeared on the outcomes and located the basis trigger. Claude had bother arising with a repair that labored effectively, and proposed a workaround with a big listing of preseeded random quantity values within the pipeline. Gemini proposed a hash-based repair reviewing my conversations with Claude, however it appeared overly advanced. As soon as I understood the issue and rejected their proposed options, I made a decision the perfect repair was less complicated than both of the AI’s ideas: a persistent RNG per simulation unit that superior naturally by means of its sequence. I wanted to grasp each the statistics and the code to guage these three choices. Believable-looking output and proper output aren’t the identical factor, and also you want sufficient experience to inform the distinction. (We’ll discuss extra about this example within the subsequent article within the collection.)
- LLMs usually overestimate complexity. At one level I wished so as to add help for customized mathematical expressions within the evaluation pipeline. Each Claude and Gemini pushed again, telling me, “That is scope creep for v1.0” and “Reserve it for v1.1.” Claude estimated three hours to implement. As a result of I knew the codebase, I knew we have been already utilizing asteval, a Python library that gives a secure, minimalistic evaluator for mathematical expressions and easy Python statements, elsewhere to guage expressions, so this appeared like an easy use of a library we’re already utilizing elsewhere. Each LLMs thought the answer can be much more advanced and time-consuming than it really was; it took simply two prompts to Claude Code (generated by Claude), and about 5 minutes complete to implement. The function shipped and made the software considerably extra highly effective. The AIs have been being conservative as a result of they didn’t have my context concerning the system’s structure. Expertise informed me the mixing can be trivial. With out that have, I might have listened to them and deferred a function that took 5 minutes.
- AI is commonly biased towards including code, not deleting it. Generative AI is, unsurprisingly, biased towards era. So after I requested the LLMs to repair issues, their first response was usually so as to add extra code, including one other layer or one other particular case. I can’t consider a single time in the entire mission when one of many AIs stepped again and stated, “Tear this out and rethink the strategy.” The best classes have been those the place I overrode that intuition and pushed for simplicity. That is one thing skilled builders study over a profession: Probably the most profitable adjustments usually delete greater than they add—the PRs we brag about are those that delete hundreds of strains of code.
- The structure emerged from failure. The AI instruments and I didn’t design Octobatch’s core structure up entrance. Our first try was a Python script with in-memory state and quite a lot of hope. It labored for small batches however fell aside at scale: A community hiccup meant restarting from scratch, a malformed response required guide triage. Lots of issues fell into place after I added the constraint that the system should survive being killed at any second. That single requirement led to the tick mannequin (get up, test state, do work, persist, exit), the manifest file as supply of fact, and your entire crash-recovery structure. We found the design by repeatedly failing to do one thing less complicated.
- Your growth historical past is a dataset. I simply informed you many tales from the Octobatch mission, and this collection can be stuffed with them. Each a kind of tales got here from going again by means of the chat logs between me, Claude, and Gemini. With AIDD, you’ve gotten an entire transcript of each architectural choice, each fallacious flip, each second the place you overruled the AI and each second the place it corrected you. Only a few growth groups have ever had that stage of constancy of their mission historical past. Mining these logs for classes realized seems to be one of the crucial priceless practices I’ve discovered.
Close to the top of the mission, I switched to Cursor to verify none of this was particular to Claude Code. I created contemporary conversations utilizing the identical context recordsdata I’d been sustaining all through growth, and was capable of bootstrap productive classes instantly; the context recordsdata labored precisely as designed. The practices I’d developed transferred cleanly to a distinct software. The worth of this strategy comes from the habits, the context administration, and the engineering judgment you carry to the dialog, not from any explicit vendor.
These instruments are transferring the world in a route that favors builders who perceive the methods engineering can go fallacious and know strong design and structure patterns…and who’re okay letting go of management of each line of code.
What’s subsequent
Agentic engineering wants construction, and construction wants a concrete instance to make it actual. The following article on this collection goes into Octobatch itself, as a result of the way in which it orchestrates AI is a remarkably shut parallel to what AIDD asks builders to do. Octobatch assigns roles to totally different processing steps, manages handoffs between them, validates their outputs, and recovers once they fail. That’s the identical sample I adopted when constructing it: assigning roles to Claude and Gemini, managing handoffs between them, validating their outputs, and recovering once they went down the fallacious path. Understanding how the system works seems to be a great way to grasp orchestrate AI-driven growth. I’ll stroll by means of the structure, present what an actual pipeline appears like from immediate to outcomes, current the information from a 300-hand blackjack Monte Carlo simulation that places all of those concepts to the check, and use all of that to exhibit concepts we are able to apply on to agentic engineering and AI-driven growth.
Later articles go deeper into the practices and concepts I realized from this experiment that make AI-driven growth work: how I coordinated a number of AI fashions with out dropping management of the structure, what occurred after I examined the code in opposition to what I really meant to construct, and what I realized concerning the hole between code that runs and code that does what you meant. Alongside the way in which, the experiment produced some findings about how totally different AI fashions see code that I didn’t count on—and that turned out to matter greater than I believed they might.
