# humaineeti agentic ai blog > AI engineered for your business ## Posts - [Is Your AP Process Agent Ready or Just Automation Ready? Agentic AI in Accounts Payable for Indian Finance Teams](https://blog.humaineeti.ai/agentic-ai-accounts-payable-india/): Priya has been head of finance at an Indian FMCG company for the last 5 years. Apart from her strategic work, she leads the team that runs AP operations, processing around 8,000 invoices a month. The invoices come from a fragmented vendor base: old and new vendors, transporters, SMBs, enterprises and so on. Every week, her OCR-based automation and tedious manual process flagged over 700 exceptions, like mismatched PO quantities, GRN mismatches, GST code variations, HSN/SAC anomalies, TDS mismatches and vendor bank account changes. Her team cleared the revalidation and compliance queue by the end of each week. Before payables could… - [RAG engineering: your problem is almost always retrieval quality](https://blog.humaineeti.ai/rag-engineering-retrieval-quality/): When a RAG system gives bad answers, teams blame the model. Usually the fault is upstream, in retrieval. Here is how to think about RAG retrieval quality and fix the part that actually matters. - [Data sovereignty in India: the case for keeping inference in-country](https://blog.humaineeti.ai/in-country-inference-data-sovereignty-india/): For regulated Indian enterprises, it increasingly matters not just what an AI model does but where it runs. Here is a grounded look at data sovereignty in India and why in-country inference is becoming a real requirement. - [Naming an AI company when every good word is already taken](https://blog.humaineeti.ai/naming-an-ai-company-brand-case-study/): Naming an AI company in 2026 means fighting for space in the most crowded naming category in tech. A short, honest case study on how to pick a name that means something and lasts. - [Responsible AI in India: what NITI Aayog's seven principles actually ask of you](https://blog.humaineeti.ai/responsible-ai-india-niti-aayog-principles/): Before the sector-specific rulebooks, NITI Aayog laid down seven principles for responsible AI in India. They remain the clearest statement of what good looks like, and they are worth understanding properly. - [LangGraph vs CrewAI vs AutoGen: choosing an agent orchestration framework](https://blog.humaineeti.ai/langgraph-vs-crewai-vs-autogen-orchestration-frameworks/): LangGraph, CrewAI and AutoGen solve multi-agent orchestration in genuinely different ways. This is an honest comparison of the three, and how to pick without regretting it in six months. - [The RBI FREE-AI framework, explained for people who have to act on it](https://blog.humaineeti.ai/rbi-free-ai-framework-explained/): In August 2025 the Reserve Bank of India released the FREE-AI framework: seven guiding principles and 26 recommendations for responsible AI in finance. Here is what it says and what it means for regulated entities. - [One smart agent will not save you: why orchestration is the real moat](https://blog.humaineeti.ai/multi-agent-orchestration-enterprise-moat/): Models keep getting better and cheaper. What does not commoditise is deciding which agent acts, in what order, with which permissions. In 2026 the winners are building systems of agents, not chasing the single best one. - [The new enterprise MCP spec moves the hard part onto your plate](https://blog.humaineeti.ai/enterprise-mcp-spec-security-guide/): MCP is going stateless and cloud-native with the 2026-07-28 release. That makes enterprise-scale agents possible, and it hands authentication, authorization and transport security back to whoever builds the servers. That is you. - [AI agent evaluation: why the final answer is not enough](https://blog.humaineeti.ai/ai-agent-evaluation-trajectory-metrics/): Grading an agent only on its final answer misses most of what can go wrong. Real AI agent evaluation looks at the trajectory: the path of tool calls and decisions the agent took to get there. - [Evaluation as a service: should you build your eval stack or buy it?](https://blog.humaineeti.ai/evaluation-as-a-service-build-vs-buy/): Every team shipping LLM features eventually needs a real evaluation setup. The build versus buy question for evaluation as a service is less obvious than it looks. Here is how to actually decide. - [When DPDP meets agentic AI: consent stops being a checkbox](https://blog.humaineeti.ai/dpdp-agentic-ai-consent-compliance/): India's data protection rules are arriving just as enterprises hand real decisions to autonomous agents. That collision turns consent, purpose limitation and accountability from policy statements into engineering problems. - [MongoDB Atlas hybrid search with $rankFusion: a practitioner's guide](https://blog.humaineeti.ai/mongodb-atlas-hybrid-search-rankfusion/): MongoDB Atlas now combines full-text and vector search in a single $rankFusion stage using reciprocal rank fusion. Here is how it works, how to weight it, and where it fits in a production RAG stack. - [Buy, build, or orchestrate? How build is bypassing the 3C grid](https://blog.humaineeti.ai/build-vs-buy-3c-grid-enterprise-ai/): The 3C model scores AI initiatives on Capability, Complexity and Criticality. The framework still holds, but one of its three axes has started behaving differently, and it changes which processes are worth buying versus building. - [Introducing humaineeti: agentic AI, engineered to earn its keep](https://blog.humaineeti.ai/introducing-humaineeti-agentic-ai-enterprise/): humaineeti engineers governed, outcome-driven agentic AI and generative AI for the enterprise — built for production and trusted from day one. Here is what we do, how we ship, and why the name matters. ## Optional - [Agent (MCP protocol)](websites-agents.hostinger.com/blog.humaineeti.ai/mcp) [comment]: # (Generated by Hostinger Tools Plugin)