Guides · Sourced, practical, ungated
elan1 guides are long-form, sourced explainers for teams putting AI agents to work on real records. They cover AI agent governance, human-in-the-loop approval, audit trails, MCP security, the EU AI Act, India's MCA audit trail rule and AI governance guidelines, Tally to ERPNext migration, and AI for chartered accountants.
Each guide is written to be useful whether or not you ever use elan1. Every date and threshold is checked against the primary source and marked with the month it was checked.
The controls that apply whichever agent, model or vendor you use: governance, human approval, audit trails and MCP.
11-minute read · Updated 10 Oct 2026
AI agent governance is the set of controls that decides what an AI agent may change, who must approve it and what evidence is kept. For agents that write to business systems, it works best at the write itself: one path every change must take, with rules, a named approver and a tamper-evident record.
10-minute read · Updated 10 Oct 2026
Human-in-the-loop for AI agents means a named person decides before a consequential change is made, with enough context to say no. It fails when approvers rubber-stamp unreadable requests. It works when approval is reserved for changes above a risk threshold, the approver is not the requester, and each decision is recorded.
10-minute read · Updated 10 Oct 2026
An AI agent audit trail is a record of every action an agent proposed, the rules that were checked, who approved it and what happened, linked so later changes can be detected. Most teams log tool calls but not the policy decision. A useful trail records both, including refused actions.
10-minute read · Updated 10 Oct 2026
Securing an MCP server whose tools can write means treating each tool by what it can change. Separate read, stage and act tools, keep write credentials away from the agent, require approval for act tools above a risk threshold, and record each act call with the policy decision.
What the EU AI Act, India's MCA audit trail rule, RBI's FREE-AI framework and MeitY's guidelines ask for when AI acts.
10-minute read · Updated 10 Oct 2026
The EU AI Act does not regulate AI agents as a category; it regulates AI systems by risk. An agent used in a high-risk area, such as credit scoring or employment, must log events automatically (Article 12), support human oversight (Article 14), and its deployer must keep those logs (Article 26).
10-minute read · Updated 10 Oct 2026
India's MCA audit trail rule, the proviso to Rule 3(1) of the Companies (Accounts) Rules, 2014, requires companies to use accounting software that records an audit trail of every transaction, with an edit log of each change and its date, that cannot be disabled. Auditors report on it under Rule 11(g).
10-minute read · Updated 10 Oct 2026
RBI's FREE-AI framework, published in August 2025, sets out principles and recommendations for responsible AI in Indian financial services, and MeitY's India AI Governance Guidelines of November 2025 set principles for all sectors. Neither is a single statute; both expect board oversight, risk-based controls, human accountability and auditable records.
Running books, compliance and financial-crime work in India with AI that proposes and people who approve.
10-minute read · Updated 10 Oct 2026
Moving from Tally to ERPNext means exporting masters and balances from Tally, mapping them to ERPNext's chart of accounts, parties, items and tax templates, importing them in a dry run, reconciling the trial balance and GST figures, and going live only after your chartered accountant signs off the tie-out.
10-minute read · Updated 10 Oct 2026
Chartered accountants in India can use AI to draft entries, reconcile GST data and prepare returns, provided every change to a client's books is reviewed, attributable and recorded. The safe pattern is an AI agent that proposes, a CA who approves consequential changes, and an audit trail the CA can read.
10-minute read · Updated 10 Oct 2026
AI can triage AML alerts and KYC reviews by scoring each case and routing it by risk, provided its confidence is calibrated on the institution's own cases. High-confidence low-risk cases can be prepared automatically; everything else goes to an analyst, and every decision, with its route and reviewer, is recorded.
Method
Long-form, dated and signed, so you can check every statement and see when it was last true.
Regulations are cited from EUR-Lex, the Ministry of Corporate Affairs, RBI, MeitY and the GST portal; standards from ISO and NIST; protocols from their own specifications. Each guide lists its sources at the end.
The guide explains the problem and the controls for any team and any vendor. One section, “How elan1 handles this”, says what our product does, in plain terms and at the assurance level where each claim is true.
Every guide shows when it was published and updated. Statistics come only from public sources; worked examples use the fictional Kolam Components and are labelled as illustrative specimens, not customer data.
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The walkthrough follows one AI agent write from the request to the proof record: the rule check, the hold for a named approver, the commit and the record.