The latest articles on Forem by Adarsh Agrahari (@adarsh_agrahari_47f6e1681). https://forem.com/adarsh_agrahari_47f6e1681 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3823501%2F686f5fbc-263a-44ac-9fba-f72bf22715b9.png https://forem.com/adarsh_agrahari_47f6e1681 en Adarsh Agrahari Sun, 15 Mar 2026 18:53:48 +0000 https://forem.com/adarsh_agrahari_47f6e1681/most-coding-agents-are-still-too-stateless-for-real-software-workflows-4320 https://forem.com/adarsh_agrahari_47f6e1681/most-coding-agents-are-still-too-stateless-for-real-software-workflows-4320 <p>Inside a single prompt… coding agents can look impressive.</p> <p>Across longer software workflows… they still get brittle.</p> <p>That was the repeated frustration behind <strong>Nexus Prime</strong>.</p> <p>I kept seeing the same pattern:</p> <ul> <li>context drifted across steps</li> <li>prior decisions got lost</li> <li>execution became messy</li> <li>too much depended on one growing prompt</li> <li>parallel work became hard to isolate cleanly</li> </ul> <p>The issue did not feel like raw model quality.</p> <p>It felt like a systems problem.</p> <h2> The real gap </h2> <p>Most coding agents are still too <strong>stateless</strong> at the workflow level.</p> <p>They can generate well in the moment.<br><br> But they struggle to carry forward the right context over time.</p> <p>That matters more than it sounds.</p> <p>Because real software work is rarely one step.</p> <p>It is:</p> <ul> <li>inspect code</li> <li>recover prior context</li> <li>choose the right files</li> <li>make changes</li> <li>verify what happened</li> <li>continue without silently losing state</li> </ul> <p>Once the workflow gets longer… the cracks show up.</p> <h2> What I built </h2> <p>I built <strong>Nexus Prime</strong> to explore that missing layer.</p> <p>It is a <strong>local-first control plane for coding agents</strong> built around:</p> <ul> <li>session bootstrap with memory recovery</li> <li>token-aware context assembly</li> <li>orchestrator-first execution</li> <li>reusable skills… workflows… hooks… and automations</li> <li>runtime truth surfaced in a dashboard</li> <li>isolated git worktree execution for bounded parallel work</li> </ul> <p>The goal is not to make agents sound smarter.</p> <p>The goal is to make them more usable over longer horizons.</p> <h2> What changed in my thinking </h2> <p>Building this pushed me toward a stronger belief:</p> <blockquote> <p>The next leap in coding agents may come less from better models alone<br><br> and more from better systems around memory… continuity… orchestration… and execution boundaries.</p> </blockquote> <p>In other words:</p> <p><strong>The problem with most coding agents is not just intelligence. It is state.</strong></p> <h2> Why bounded execution matters </h2> <p>One thing that became clearer while building Nexus Prime is that unbounded agent workflows do not just get expensive.</p> <p>They get misleading.</p> <p>Stale context accumulates.<br><br> Older assumptions bleed into later decisions.<br><br> The prompt becomes a blurry substitute for explicit state.</p> <p>That is why I became much more biased toward:</p> <ul> <li>bounded execution</li> <li>explicit memory recovery</li> <li>isolated worktrees</li> <li>runtime traces over vague conversational continuity</li> </ul> <p>If context has to be compacted… durable state should move somewhere explicit first.</p> <p>Otherwise compaction just hides distortion.</p> <h2> Early signal that this problem is real </h2> <p>After launching Nexus Prime… the most interesting early signal was not just usage.</p> <p>It was recognition.</p> <p>Different technical communities kept coming back to the same idea in different words:</p> <ul> <li>coding agents need better state</li> <li>memory matters more than people think</li> <li>orchestration and runtime discipline are the missing middle layer</li> </ul> <p>That was useful confirmation that this is not just a personal frustration.</p> <h2> What I am curious about next </h2> <p>I think we are still early in understanding what the right systems layer for coding agents looks like.</p> <p>Some open questions I am thinking about:</p> <ul> <li>what should count as durable state vs disposable context</li> <li>how much memory recovery should happen automatically</li> <li>when should orchestration choose a workflow vs a specialist vs a tool</li> <li>what is the right balance between flexibility and bounded execution</li> <li>how much of agent usefulness comes from model quality vs control-plane design</li> </ul> <h2> If you are building in this space </h2> <p>I would love to hear how you are thinking about:</p> <ul> <li>memory across workflows</li> <li>bounded execution</li> <li>context recovery</li> <li>worktree-based parallelism</li> <li>practical coding-agent architecture</li> </ul> <p>Nexus Prime is here if you want to explore it:</p> <ul> <li>Website: <a href="https://nexus-prime.cfd" rel="noopener noreferrer">https://nexus-prime.cfd</a> </li> <li>GitHub: <a href="https://github.com/sir-ad/nexus-prime" rel="noopener noreferrer">https://github.com/sir-ad/nexus-prime</a> </li> <li>Product Hunt: <a href="https://www.producthunt.com/products/nexus-prime" rel="noopener noreferrer">https://www.producthunt.com/products/nexus-prime</a> </li> </ul> <p>If you have hit similar problems… I would be especially curious where your current setup breaks.</p> <p><strong>Question for people building with coding agents:</strong><br><br> Do they mainly need better models from here… or better systems around state… continuity… and execution boundaries?</p> ai opensource mcp agents