The Evolution of Coding Agents

Coding agents are evolving as harnesses get absorbed into models, with specialized agents rising to fill gaps where baseline models lack data, according to an industry analysis. The commoditization of models shifts value to the harness layer, which will become less invasive over time as models improve through user feedback. Specialized startups like Soar.app, Basis AI, Tsenta, and Greptile can build data moats by combining frontier models with proprietary tooling for specific domains.

The harnesses increasingly get absorbed into the model. We're starting to see a commoditization of models, and a value accrual on the harness layer i.e the layer on top of the model which controls where context goes in . This will keep happening, but that layer will keep moving away from the model. A big part of how the models get better is through people using them. That behavior feeds back into the models - how to manage tools, compaction, memory, skills, etc. Think about when you have a long running chat with an agent, and your harness forces compaction at certain intervals. All that data is feeding back into the models, so they will learn better how to have longer conversations. Overtime, the harnesses will keep getting higher level, and the models will do more and more heavy lifting. This will be similar to the Forrest Gump moment we had with workflows - the harness will get less and less invasive. We are already seeing hints of this with claude code deleting large parts of their system prompt. 2. The agents get much better because of the models, but not existentially threatening anytime soon. Humans are feeding AI so much training data that they're going to get better. The reasoning chains will become better over long horizons & the decision making quality will increase. It is unclear if there is a cap on how good the models get. ASI, RSI etc. still feel like a sci-fi movie. My prediction is - AI will get much better than it is today, but it is fundamentally blocked. AI is modeled on an abstraction of reality created by humans, not actual reality. It does get a taste of reality through verifiable tasks like coding & math if you subscribe to the notion that the consequences of mathematics is encoded into reality , but for a large part AI does not experience reality like we do. This is a very Sutton based argument of whether the model architecture today hints at real intelligence or complex imitation machines. My perspective is that its somewhere in the middle, but I would agree that there would be a massive unlock if we let AI experience reality for itself and continually learn, and until we get there we might be fundamentally limited. There's much more compute, algorithmic efficiencies, and data that we are yet to throw at these models, which will give us great gains. 3. There will be a rise of specialized agents. The implication of 1 and 2 is that the baseline agents that the labs sell will rapidly improve. And while they get better generally, they will struggle to perform on domains where they lack data. For eg, if you try to get an agent to book you a trip to the Maldives, you would have to hand hold it at many steps - the experience would be sub-par. That's why startups like Soar.app travel agent can do tremendously well. They build all the tooling, infra, and data around travel, then use a combination of the best frontier models to give people a phenomenal travel agent experience. Over time, as people use their app, they will build a data moat. You can take a model, and build a bunch of proprietary tooling and infrastructure around it, and then let other people interact with your agent to achieve a certain goal. If your specialized agent is significantly better at a task than what they get out of the box with codex, you've created value. You have the tailwind of intelligence on your side, so your focus becomes in getting access to the data and capabilities that elevate the baseline agents at a domain, which is why I suspect there will be an influx of successful specialized agent companies You can even take advantage of combining different models with different strengths. The levers are harness + context + tools + capabilities. We see this with Basis AI accounting , Tsenta job hunting , Greptile code review . There are things that the model providers will largely win because the baseline agent simply solves the problem. But if the task is consequential enough, there will be a market that cares for supreme quality, and then you have to beat the model by enough of a margin for those people to care. For eg. OCR a small margin of error is devastating . The last variable to consider here is the market size - because if it is large enough and easy enough on their path , the labs will also be compelled to focus some energy and try to dominate. This doesn't mean you can't win though - Devin is a great counterexample. Its just worth noting. 4. Smaller models will be used more and more. Right now tokens of frontier models are outputted at 30 tokens/sec, and with Cerebras they will presumably be outputted 30x faster. Devins SWE 1.7 model feels magical. Rate of change of complex work is bottlenecked by token output, and this will go away. More interestingly, the cost per unit intelligence is decreasing by a factor of 10 every year due to knowledge distillation. To be clear, I am not referring to token cost. SoTa models will probably get more expensive, but what you pay for some amount of intelligence today will get 10x cheaper. We don't need Fable 5 for most tasks, its severely overkill. Models getting 10x cheaper is a massive tailwind for the democratization & diffusion of AI. 5. Using a computer becomes a novelty. I know this is a hot take, but bear with me. Almost everything you do on a computer is for a means to an end. Computing let us write programs that ran on the machine to perform millions of operations in seconds, and the internet gave us a network for these programs to be distributed. Databases let us store information. Boiling it down to these three categories - writing programs to do work, being able to share it to everyone, and storing memory - inadvertently became all we needed. So, software started eating the world. Everything we could possibly digitize, we did, because it was convenient to do so. We did have a bottleneck though - software engineers were scarce & expensive. Thats gone now. Not only can AI agents write software, they can use computers and use this software. You can ask Fable 5 to go to the San Francisco Tennis Reservation System and book a tennis court for you without having to click through all the pages. Looking through your inbox is a thing of the past, an agent can look through it for you, summarize & manage it, and even draft messages for the important stuff. Browser-use & computer-use are the worst way to actually use software. Its token intensive and slow - the agent has to look at the DOM html and the screen to figure things out. But the internet was built over 20 years, and not everyone is going to ship a shiny MCP and programmatic access for agents to come visit. You still have several websites that conditionally render .cshtml as you scroll. And worse - you have several sites that don't actually want you using AI agents to access them - so they try to block agents with heinous captchas. The writing is on the wall that software from here-on-forward will be built with the end-users as agents, and most companies will be heavily incentivized to create the access for agents to be successful, which they can do through an mcp or programmatic access. There will be two camps. websites / software that want to be agent friendly the ones that don't. Social media sites like Twitter & data-heavy sites like Zillow obviously don't want agents crawling around, so they'll do everything in their power to stop that from happening, while even a random parts supplier in Indiana will want some way for agents to be able to buy parts. The middle ground is then people that haven't caught up yet, and people that want to access the sites through agents which don't want to be accessed. While the shift takes place, and products and software become increasingly AI friendly, browser agents and computer use also become better - so most of the digital world becomes accessible to AI agents, and we'll probably have a cat and mouse game on the sites that want to ban agents - which has been happening with deterministic bots and scrapers anyways. Given that most of the digital world is accessible to AI agents, and this only increases over time, you as a human being will start to use your computer to do things less and less, until one day you will be doing all of your work through a very nice chat interface. Plug: I'm building this at www.puffle.ai Your computer becomes a dumb pipe, and using your computer becomes a novelty. The AI agents will have their own computer which lives in the cloud, so they don't shut down when you close your laptop. You will use your device to communicate with your agent, but it won't live on your device. It will do all your work for you. 6. Software value hinges on data, infra, & networks The only software products that are valuable will be those that have scarce data, need heinous infrastructure setup, or have network effects. A pure SaaS is cooked, because even if you were genuinely creative enough to find the problem, it will be almost instantly replicable. There's already tons of open source alternatives for many products we use today. SaaS's that have millions of folks using them, and large enterprises, are somewhat cushioned from this because they had to solve major scale issues - but even then. When software inevitably becomes near free, how can anyone justify paying any money to a managed solution. I recently saw a post by ShadCN complaining about how people replicate creative UI projects in an evening. Ultimately, businesses are about value add & value capture. I'm sure nice UI components will add value before prediction 5 becomes a certainty, but you can't capture significant value selling it because of how easily replicable it is. 7. The age of the physical world is back Software had great valuations because of its multiplicity. You could scale up software so easily with such high margins that it became the most valuable industry. But now, moving bits is easy, and its only getting easier. Moving atoms, however, is hard. And as our digital ether becomes abundant, the physical world becomes a glaring bottleneck. You create value by giving society what it needs, what it finds hard to get, and largely that is the physical world. We will have self-improving self-maintained software that spins up in milliseconds, whether disposable or permanent. 8. Scientific Discoveries This one is very tricky. I don't have a well thought out opinion on this, but I think at the very least AI will start to find a lot of correlations within pieces of work that we've missed. We've already seen this, with AI solving some of Erdol's unsolved problems. Whether AI is actually able to come up with completely new and novel theories like complex numbers, the theory of relativity etc. is almost more of a philosophical nature about the ontology of reality & knowledge. If all innovation on the model front were to stop today, most professions that would largely get absorbed. Today - the following professions can be automated completely - customer service, any clerical work, secretaries and executive assistants, IT support, HR, supply planning, records, mail, admin, etc, which together combine ~ 5% of the GDP. AI is good enough to do these tasks now, because these tasks have well structured inputs and verifiable outputs. For most of the population, these tasks are fairly mechanical and not as complex. Assuming the models do get better, complexity also stops being a barrier, and the higher skills operations start to get eaten in - engineering, architecture, research, design. The top percentage of humans at their fields will still be valuable. Specifically, humans that are good at collapsing an extremely arbitrary action space into long chains of executable tasks using taste, design, and creativity, are fairly irreplaceable. As AI models get better, more and more humans fall below the rising sea level of value add. Over the next decade, as AI diffuses into the economy, the shift will become much more evident. We're only a few years in.