The gaps in governance. The limits of RAG. Why SLMs matter. The failure economics nobody calculates. The unglamorous engineering that makes agents work in production. We write about all of it — from a small team in India, building in the open.
The gaps in agentic AI — governance, failure economics, RAG limits, SLMs, and what the hype misses.
The microservices playbook — retry, circuit breaker, saga, graceful degradation — assumes deterministic services. Agents are non-deterministic autonomous actors. Here's why the patterns we know don't transfer.
Read more →When a microservice fails, you lose uptime. When an agent fails, the failure cost can be 25,000x the success cost. An agent that costs $0.02 to run correctly can cost $515 when it's wrong.
Read more →Logs tell you what happened. Audit trails tell you why, whether it was allowed, who's responsible, and whether you can prove it. If your audit trail doesn't include perception, reasoning, and policy checks — you have logs.
Read more →At current LLM prices, running 100 agents per employee costs $2-5/day. Less than coffee. A 10,000-person company would have 1,000,000 agents. Without governance, this is a nightmare. With governance, it's a superpower.
Read more →Most teams jump from Prototype straight to Production. They skip the evaluation suite, the governance gates, and the launch readiness review. Then they're surprised when the agent does something wrong.
Read more →You start with 50 lines of agent logic. You end up with 800 lines of agent logic and 1,200 lines of glue code. The glue code is 70% of your codebase — and 70% of your maintenance burden.
Read more →"It works" is the most dangerous phrase in agent engineering. It works for what inputs? Under what conditions? For how long? Against what adversarial prompts? Without an evaluation suite, you don't have a product.
Read more →Silicon Valley assumes fiber, $20/month, English, iPhone. India assumes 2G, $2/month, 22 languages, shared devices. Different constraints produce different technology — and better products.
Read more →Using an AI model to govern another AI model is like using a security camera that only detects intruders 92% of the time. Governance should be deterministic. AI detects, rules decide.
Read more →Before you build a multi-agent system, ask: can a single agent with good tools do the job? Multi-agent systems add coordination overhead, communication failures, and cascading errors.
Read more →Your agent will break something. Not "might." Will. The question is: when it happens, will you know what to do? Detect, contain, assess, remediate, prevent, communicate.
Read more →Before you build something, ask: will this matter in 10 years? The 10-year filter eliminates features that are impressive but ephemeral. What remains is the work that matters.
Read more →We wrote a 16-page fairytale about our programming language. It got more engagement than the technical documentation. Stories are how humans understand new concepts.
Read more →We're repeating the same mistake from every technology shift — taking old paradigms, adding "AI agent," and shipping it. Here are the 5 mistakes teams make when building agents.
Read more →Before you build an agent, ask five questions. If you can't answer all five, you don't need an agent — you need a simpler solution.
Read more →Each era of computing introduced a new relationship between human and machine. Agents are the fourth era — and they're fundamentally different from everything that came before.
Read more →Trust in AI agents isn't a feeling. It's an equation: Trust = Transparency × Accountability × Predictability. It's multiplicative — if any factor is zero, trust is zero.
Read more →Building a DSL for AI agents taught us about type systems, compiler design, and why declarative beats imperative when the domain gets complex enough.
Read more →The engineering behind agentic solutions — what we built, what broke, and what we open-sourced.
We open-sourced two projects that work as a pipeline: AXON (a typed DSL for defining agents) and AgentOps Mesh (a governance control plane). MIT licensed, no paid tier, no signup. Browser playground included.
Read more →Every team building AI agents hits the same wall: the agent works in the demo, but "is it safe to deploy?" has no answer. We built both sides of the equation — and made them work together in the browser.
Read more →70% of developers who star your repo never actually try it. They fall off in the install funnel. We built a playground where you open a URL and try everything. Zero activation energy.
Read more →Open-sourcing infrastructure isn't charity. It's strategy. The infrastructure builds the audience. The intelligence builds the business.
Read more →AXON defines agents. AgentOps Mesh governs them. Together they create a pipeline where the agent definition is the governance contract.
Read more →Governance is the layer everyone knows is missing but no one wants to build. We built it and open-sourced it. Here's why.
Read more →AI literacy, science education, and building tools that work for the next generation.
Stories are how humans understand new concepts. Every culture teaches through stories. Yet in technology, we default to specifications. Start with the story.
Read more →Half the world doesn't have reliable internet. But AI education assumes everyone is online. We built educational platforms that work offline — and the constraint produced better design.
Read more →Free forever is a design constraint, not a pricing strategy. When you can't rely on subscription revenue, every decision changes — and the constraints produce better design.
Read more →STEM without culture produces technicians, not thinkers. Culture without STEM produces nostalgia, not progress. We need all four — in 22 languages, free forever.
Read more →AI literacy isn't coding. It's understanding that AI isn't magic, AI can be wrong, AI has biases, and AI raises ethical questions. Start at 10. Start with intuition.
Read more →Science education in India is memorization-first. We built 5 interactive science labs that let children feel physics, chemistry, and biology — not just memorize formulas.
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