Harness Engineering for Reliable AI Agents

Vibe coding is not the opposite of engineering. It is the starting point. The problem begins when a fast demo is treated like a finished system. AI can help teams move faster from idea to prototype. But once real users, edge cases, security, testing and maintenance enter the picture, speed alone is not enough. That is where engineering judgment matters. #VibeCoding #AIEngineering #SoftwareEngineering #AIAutomation #ProductDevelopment #TechLeadership #Crescaler

AI-assisted development accelerates pull requests and code generation. But many teams are discovering the time savings reappear later in debugging, incident response, and production validation. The issue is not generation speed. It’s verification depth. When engineers review AI-generated code they did not fully design, edge-case failures and architectural inconsistencies become harder to detect early in CI/CD. This changes where QE effort is required. Faster code generation does not reduce engineering responsibility. It redistributes it. Where has AI shifted workload inside your delivery pipeline? #QualityEngineering #CICDpipelines #QEautomation

Just wrapped up "Token Intelligence & Context Engineering for Engineering Excellence" through UST's Journey program. Most of what I do day to day already touches this — leading a team that's building GenAI-driven analytics on top of manufacturing data, using LLM APIs and Copilot to speed up root-cause analysis, that kind of thing. But there's a real gap between using an LLM well and actually understanding what's happening with context windows, token budgets, and how you structure input to get consistent output instead of a coin flip. This course was about closing that gap properly instead of picking it up in pieces from documentation and trial and error. The part I found most useful: context engineering isn't really a prompting trick, it's an architecture decision. How you retrieve, structure, and trim what goes into the model changes cost, latency, and reliability just as much as the model choice does. That's directly relevant to the kind of AI tooling I'm building right now. Small credential, but a good one to have actually sat down and gone deep on rather than assumed I already knew. #GenerativeAI #ContextEngineering #AIEngineering #ContinuousLearning

A loop that ran perfectly every morning for weeks can still burn your test folder down on one bad day. Same model. Same prompt. But today the agent hits a strange error, decides deleting the tests is the fix, and reports back: "Done! All tests pass." Nothing stopped it. Nothing checked it. Nothing even logged that it happened. Yahi toh asli masla hai — the model wasn't the problem. The loop wasn't the problem. The problem was everything sitting between them: the layer that decides what the agent may do, what it knows, how its work gets proven, and what happens when it goes wrong. That layer has a name. The harness. Agent = Model + Harness. The model brings the intelligence. The harness turns that intelligence into something you can actually trust. Here's the part that stopped me: I've been using harnesses this whole time without knowing it. → Claude Code is a harness → OpenCode is a harness → I've only ever run them on defaults Running on defaults means someone else decided your guardrails for you. Harness engineering is where you stop inheriting those decisions and start making them on purpose. This one builds directly on Loop Engineering — heartbeats, beats, the maker-checker split. If you haven't done that course (or agentic coding before it), this will feel like the second half of a sentence you haven't heard the start of. The bigger shift: reliability was never going to come from a better prompt. It comes from what you build around the model. What's the closest you've come to an agent making a "confident but wrong" call on its own? Day 21 of my 90-day #BuildInPublic challenge. #AIAgents #AgentFactory #AgenticAI #BuildingInPublic #Panaversity

this is exactly how i see ai fitting into software engineering. i do not care if ai writes 10,000 lines of code in a minute. i care about whether those 10,000 lines can survive my constraints. unit tests. integration tests. mutation testing. static analysis. ci/cd. monitoring. code reviews when they matter. the future is not about replacing engineers. it is about moving engineers higher up the stack. less time writing boilerplate. more time designing systems that make bad code impossible to ship. anyone can generate code. great engineers build systems that guarantee quality. https://lnkd.in/g3DUh6qZ

As autonomous developer tools and multi-agent IDEs dominate software engineering in 2026, production reliability challenges have shifted toward agent deadlocks and state memory bloat. When multi-agent systems loop through execution cycles, unpruned state contexts cause unexpected timeouts and blown token budgets. In my latest deep dive on Code With Shivam, I share how to build ephemeral sliding-window reducers and custom graph circuit breakers in LangGraph. Let me know your thoughts in the comments below! Keep coding, keep building.

Takeaway: AI can speed up development, but strong engineering fundamentals still matter. The best results come from combining AI with good architecture, code reviews, and sound engineering practices - not relying on AI alone. #AI #softwareengineering #agenticAI #devops #platformengineering https://lnkd.in/gibhSExw

I just wrote a new piece: "Everything to Know About Loop Engineering" Loop engineering is the practice of designing a system that prompts an AI agent, checks its work, and decides what to do next, instead of a person doing all three of those things by hand, one message at a time. In this article, you will learn all there is to know about loop engineering, with practical examples and real use cases. Read here: https://lnkd.in/eU4qhHeJ #ArtificialIntelligence #LoopEngineering

Prompt Engineering Is Out. Loop Engineering Is In. If you’re still focusing purely on crafting the "perfect prompt," you’re already behind. In the early days of AI coding assistants, we acted as human middleware: write a prompt, paste the code, watch it fail, and feed the error back to the LLM. Loop engineering changes the game. It shifts the paradigm from one-shot prompting to systemic iteration. Instead of tweaking prompts to get a flawless first attempt, loop engineering designs closed, automated feedback loops where the AI can: - Act: Generate code or call an API. - Observe: Run the code through real tools (compilers, linters, pytest). - Reason: Process standard, execution-level error logs. - Self-Correct: Fix the issue and try again until tests pass. Prompt engineering manages inputs. Loop engineering manages feedback. The 3 Pillars of Production-Grade Loops Building effective agentic loops isn't just giving an LLM terminal access; it's about building a controlled harness around it: - Context Pruning: Multi-turn loops quickly saturate context windows. Smart loops strip away noisy logs and keep only high-value progress states to reduce cost and latency. - Decoupled Verification: Models are biased toward their own code. Reliable loops use deterministic tools or separate, lightweight evaluator models to validate work before declaring "done." - Circuit Breakers: Infinite retry loops burn API budgets fast. Production loops need hard limits—iteration caps, budget ceilings, and fallback paths to human review. The Evolving Role of the Engineer Engineers aren't becoming obsolete; our abstraction layer is shifting. We are moving from writing syntax to acting as System Architects of Loops—designing the guardrails, test harnesses, and context boundaries that allow autonomous agents to operate safely. The real value isn't getting an LLM to guess right on the first try. It’s building the system that turns plausible guesses into verified, production-ready code. Are you implementing agentic loops in your workflow? Where are they breaking most—context bloat, test harnesses, or API costs? Drop your thoughts below! #AI #SoftwareEngineering #LoopEngineering #ArtificialIntelligence #CodingAgents #DevOps

This strongly matches what I’ve been learning while building rcode. One thing that’s becoming clearer to me: better agentic development isn’t about giving AI more and more context. It’s about giving it the right context, a clear goal, a way to verify the work, and enough autonomy to actually finish the job. That’s a big part of why I started building rcode. I didn’t want to keep stuffing everything into CLAUDE.md or repeatedly explain the same architecture, decisions, workflows, and engineering rules every new session. I wanted the knowledge to live somewhere persistent, and let agents discover what they need when they need it. The workflow I’m increasingly using looks something like: Intent → Context → Plan → Execute → Verify → Review → Fix → Re-verify And the more I work this way, the less I want one giant agent doing everything. I’d rather have specialized agents working toward the same goal — one implementing, another reviewing, another testing or challenging assumptions. One thing I’d add to this playbook 👇 Autonomy gets much more useful when your engineering knowledge survives the session. That’s the problem I’m exploring with rcode. I usually describe it as trying to build an engineering organization in a folder for AI coding agents. Still experimenting and learning a lot from it, but this post captures many of the same principles I’ve been seeing in practice. Great breakdown 👏