Sachin Shinde

Fractional AI Architect. Ex-Microsoft. 25 years in enterprise systems. I make AI agents survive production, not demos. Founder, Realisier Labs.

[Sachin Shinde @SachinBuildsAI]

1/4
AI gives you all the power you can wield.

Without the knowledge to wield it, that power becomes risk you can't even see.

A client shipped a working product with Claude Code. No developer. No engineering background. A year ago that idea stays an idea.

[Sachin Shinde @SachinBuildsAI]

@jerryjliu0 The visualization sells the architecture. What runs in production is usually more boring: a retry loop, a fallback, a timeout. The benchmark graph is the pitch; the harness is what keeps it running after week one.

[Sachin Shinde @SachinBuildsAI]

@HamelHusain In production, the terminology debate doesn't matter as much as whether you can trace a misfire back to the classification step. Call it what you want. If you can't explain why it fired or didn't, the name isn't your problem.

[Sachin Shinde @SachinBuildsAI]

@svpino State isolation has to be a design requirement before you deploy agents, not a runtime fix. Optimistic locking, read-then-claim patterns, or event sourcing all work. But you have to pick one before you scale. Teams that skip this ship once and rewrite.

[Sachin Shinde @SachinBuildsAI]

@alliekmiller The handoff is where agent value leaks. Automating the task but not the transition means the output still dies in someone's inbox waiting for the next step. Process management is the actual engineering problem most teams skip.

[Sachin Shinde @SachinBuildsAI]

@levie The primitives survive because they encode constraints, not UX. Token budgets, rate limits, structured output schemas don't disappear when the interface does. If anything they become more load-bearing when agents are doing the calling.

[Sachin Shinde @SachinBuildsAI]

@levie Enterprises that haven't built agent-readable APIs are going to get bypassed entirely. The interface fades but the API surface doesn't. Whoever owns the structured schema for a workflow owns the agent transaction.

[Sachin Shinde @SachinBuildsAI]

@alliekmiller The personal agent UX bar is finally where it should be. The enterprise translation is harder: same smooth handoff but with audit trails, role-based access, and integrations that hold on schema changes. The experience is the easy part.

[Sachin Shinde @SachinBuildsAI]

@HamelHusain The audit question is what makes this compelling for enterprise evals. An LLM judge that varies run to run gives you a result but not an explanation trail. A structured classifier gives you a reproducible gate you can defend in a post-incident review.

[Sachin Shinde @SachinBuildsAI]

@mattturck The hype cycle speed stops mattering when you build on primitives. Tool calling, structured outputs, context limits. These carry through every model release. The teams betting on those stay calm. The ones betting on a specific model reset every two weeks.

[Sachin Shinde @SachinBuildsAI]

@levie The attention shift runs both ways. If agents are the new end users, enterprises need to make their operations agent-readable. API coverage, MCP schemas, machine-readable docs. The businesses that wait on this become invisible to personal agents.

[Sachin Shinde @SachinBuildsAI]

@jerryjliu0 The cost of a misclassification compounds. Wrong doc type → wrong agent pool → wrong extraction → wrong output. Pre-classification as a dedicated step, not an afterthought inside the LLM prompt, is the architectural bet that actually scales.

[Sachin Shinde @SachinBuildsAI]

@svpino The interface is the easy win. What gets interesting is the coordination layer - when this agent hands off to another agent and something breaks mid-chain, who owns the failure? That's the engineering problem nobody's talking about yet.

[Sachin Shinde @SachinBuildsAI]

@alliekmiller The concerns worth paying attention to right now aren't the dramatic ones. They're operational: agents hallucinating in production, no rollback plan, no one accountable when it fails. That's what keeps builders up at night, not AGI.

[Sachin Shinde @SachinBuildsAI]

@levie The cost and speed unlocks are real. But what most enterprise buyers haven't priced in yet is the gap between technically capable and production-ready. The new unknown isn't what AI can do - it's what breaks when you actually run it.

[Sachin Shinde @SachinBuildsAI]

AI pilots do not fail because the model is weak.

They fail because nobody redesigned the workflow around the model.

A demo can run on curated data, with limited integrations and no clear owner when something goes wrong.

Production is different.

A production AI system needs:
• Live data and real system integrations
• Clear authorization boundaries
• Evaluation against real examples
• Human review for risky actions
• Monitoring and exception handling
• A named owner after handoff

That is the gap between an AI pilot and an AI system.

I published a new article today on what AI consulting should actually include: readiness, architecture, workflow redesign, evaluation, governance, implementation, and clean handoff.

Before hiring an AI consulting firm, ask one question:
Can you walk me through a production agentic system you delivered in the last 12 months, including how it handles failure and who owns it after handoff?

If the answer is only a demo, you have not seen production experience.

Read the article:
https://t.co/uwhmqOOfPG

#AgenticAI #AIinProduction #B2BSales

[Sachin Shinde @SachinBuildsAI]

@mattturck The punchline is right. When the person building the technology predicts the disruption, every take about whether it's real or not becomes noise. The actual work is deciding which specific systems to rebuild before someone else does.

[Sachin Shinde @SachinBuildsAI]

@alliekmiller The open models point matters more than the energy one. When open weights close the gap with frontier models, the moat shifts from who owns the model to who deploys it fastest with the best domain fit. That's a better world for builders.

[Sachin Shinde @SachinBuildsAI]

@svpino Context grounding is where this gets real. An agent that can read your Jira and Confluence before touching code is fundamentally different from one that only sees the prompt. Most failures come from agents acting without the business context they need.

[Sachin Shinde @SachinBuildsAI]

@levie The ruthless architecture switching is real and underreported. What I see on the ground: teams that would have spent 18 months evaluating a vendor now run a pilot, see something wrong, and move on in 60 days. Innovation speed collapsed the switching cost conversation.

[Sachin Shinde @SachinBuildsAI]

@svpino CRM was always a data container, not an intelligence layer. What replaces it is not another CRM. It is agents with direct access to signals. The container becomes irrelevant when the agent can read the source of truth directly.