Buying AI Land Acquisition Software? Ask the Right Questions

Building an AI SaaS in Public Part 2 I used to think multi-tenancy was just adding a tenantId to every table. It turns out that's the easy part. The hard part is making sure Tenant A can never access Tenant B's data, even when a developer makes a mistake. When you're building a multi-tenant platform, almost everything changes: • Authentication becomes tenant-aware. • Authorization becomes tenant-aware. • Background jobs become tenant-aware. • File storage becomes tenant-aware. • AI agents become tenant-aware. • Analytics become tenant-aware. • Audit logs become tenant-aware. Missing a single check might not break your application... But it could expose another customer's data. That's why I've started thinking about multi-tenancy as an architectural concern rather than a database feature. Every API, every query, every service, and every workflow should naturally operate within the current tenant's context, not rely on developers remembering to filter by tenantId. Building this has completely changed how I design backend services. How do you approach tenant isolation? Application-level checks, Row-Level Security (RLS), separate databases, separate schemas, or a hybrid approach? I'd love to hear how others solve this problem.

In this article, you will learn how to decide whether a given piece of agent functionality should be built as a tool or as a subagent, and how to avoid overengineering your agent architecture in the process. Topics we will cover include: What tools and subagents are, and the key differences between them. When a tool is the better choice, and when a subagent is worth the added complexity. https://lnkd.in/duZUn7uK

Models aren't the only layer with a knowledge cutoff. Training date sets the model's. Last crawl date sets the retrieval layer's. Only one shows up on a model card. In most implementations I've seen, a production agent making a web search call hits an index. That index was built from a crawl that predates the request. How much earlier varies, and the output rarely says. For compliance monitors and due diligence workflows, that invisible interval is where results can silently be accurate at crawl time and wrong at inference time. The failure doesn't announce itself. One boundary gets managed because it's visible. The other rarely does, because it isn't. For agents where recency is the requirement, freshness is an access architecture question. Training dates get managed. Retrieval timestamps get discovered in production. For the infrastructure side of this specifically, how the live data access layer fits into the agent stack: https://lnkd.in/gHrrZapA — P.S. Also linking the first post for anyone who wants the full arc on the access layer and audit trail question. #AIInfrastructure #AIAgents

If an AI can’t summarize your site, it won’t recommend you. 2026 website must-have: AEO (Answer Engine Optimization) • Clear, authoritative answers • Structured data (schema) • Clean architecture What’s the #1 question your customers ask before hiring you?

4 architecture choices that decide whether your AI agents actually understand your enterprise codebase. As a TL, I spend a lot of time prompting agents to understand how our trading system behaves under different conditions. These software principles are not new — they just compound at a steeper rate now that agents are reading the code too. 1. Typed schemas over loose objects. A type domain model for "Position" or "Order" tells an agent what those concepts mean with minimal context cost, instead of making it infer that from a dozen field names across three services. 2. APIs split by real use case. "Close a position" and "adjust margin" are different intents, even on the same table. Split that way and an agent guesses right the first time. Split by table and it reverse-engineers intent every time — burning tokens to get there. 3. One term, one meaning, everywhere. If "mark price" means one thing in pricing and something subtly different in risk, every grep an agent runs returns a plausible-looking wrong answer — hard for a human reviewer to catch. 4. Service boundaries that mirror business concepts. In a monorepo especially, a clean split into "API service," "risk," "matching engine" gives an agent a map instead of a maze — it reasons at the concept level instead of tracing every file by hand. It's who's reading the map now, and how much context-engineering time you burn when the map is wrong. The more explicit the domain logic, the less hand-holding the agent needs — and the less you depend on docs or RAG to fill the gap. What other architectural decisions compound this way? #EnterpriseAIAdoption

Two completely unrelated client projects landed on the exact same design decision this week, and neither team knew about the other. One is migrating years of records out of an old database system. The other is trying to use AI to read handwritten paper tickets. Different industries, different stacks, same underlying problem: what do you do with data that might be wrong, without either blocking everything on human review or silently trusting the import? Both teams landed in the same place. Migrated or AI-parsed records get a visual flag, in this case just styling them red, until a human confirms them. Once reviewed, they go back to normal. Nothing gets deleted or blocked, but nothing pretends to be verified until someone actually looked at it. What surprised me was how obvious it felt in hindsight. Our default instinct with imported or inferred data tends to swing to an extreme: treat it as fully trusted or refuse to use it until every record is checked by hand. The middle path, visible but not blocking, doesn't require perfect confidence. It just requires honesty about what hasn't been checked yet. With more AI-assisted parsing and one-time migrations showing up in client work, I expect to see this pattern more often. Have you landed on something similar? I'm curious how other teams surface unverified data without stalling the whole workflow. #SoftwareDevelopment #SoftwareEngineering #Architecture #DeveloperExperience #Programming

GEOrank is an open-source workbench for Generative Engine Optimization. It helps teams diagnose AI search visibility, turn insights into Q&A and action plans, expand keyword assets, generate structured content tools, and manage the workflow through a self-hosted admin console. Teams can use it as an internal GEO toolbox, apply it to their own diagnostic and analysis workflows, or self-host and extend the code to deliver GEO tools and services to clients. This repository includes the product code, engineering structure, configuration templates, demo data, and the built-in public expert profiles used by the experts channel. It does not include private production data, non-public expert content, real tutorial assets, user conversations, generated customer plans, keyword packs, database dumps, object storage files, or API keys.

𝗪𝗲 𝗥𝗲𝗯𝘂𝗶𝗹𝘁 𝗢𝘂𝗿 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 I decided to throw away most of our first autonomous agent. At Eko, we build agents that perform real work in financial systems. These agents do not just draft replies. They read requests, make decisions, and change production records. When your software moves money, "usually right" is not enough. It must be correct every time. Our first version used a lexical architecture. It relied on keyword matching and a knowledge base to find answers. It worked well for a while, but we hit a ceiling. We faced two major problems. First, we lacked authority. A probabilistic system should not have the power to write to financial records. Relying on prompts to keep an agent in bounds is dangerous in a regulated environment. Second, the system got worse as we added more data. As we taught it new tasks, the keyword matching became crowded. The right answer started getting lost in the noise. We stopped iterating and started rebuilding. Our new rule is simple: intelligence in the agent, authority in the API. The model is smart enough to understand a request. However, it holds no power. It produces a single typed proposal. A separate, deterministic core holds all the authority. This core does not trust the model. It validates the proposal, checks permissions, writes an audit record, and then executes the task. The smart part can be wrong without being dangerous. The part that acts is boring and provable. We also changed how the agent learns. We stopped using retrieval. Instead of searching a database at runtime, we compile our knowledge into a single catalog. We give the model the entire menu at once. This costs more in tokens, but it works better. A retrieval step can hide the right answer if it ranks poorly. A full menu cannot hide anything. Adding new knowledge now makes the system better instead of worse. We also added essential safety features: - An append-only audit log that writes before any action occurs. - Exactly-once execution with rollback capabilities. - A precision wall that blocks any release that fails specific test cases. The results are clear. Our new system is more accurate and much safer. The authority lives in tested code rather than in hopeful prompts. You should not rewrite because your first version is bad. Rewrite when you see that the structure of your system cannot reach your goals. Source: https://lnkd.in/gJNpmUUY Optional learning community: https://t.me/GyaanSetuAi

Rent rolls have quietly radicalized me on how document AI should be built. Every property manager seems to invent their own vocabulary. “Net rent” in one document is “base rent” in another. Totals may sit neatly at the top, or disappear into a footnote. One row may contain a nested charge table. Another may continue on the next page. Bank statements are not much better. Every bank has its own opinion about what a table should look like. The uncomfortable truth is that these documents were never designed for software. They were designed for human eyes. And humans are very good at quietly filling in gaps with context. Software is not. So when people ask why we don’t just hand the whole PDF to one big model and let it figure things out, my answer is simple: We tried. Everyone tried. It works until the documents get long, the layouts get weird, the volume goes up, and the output needs to be trusted. What works better is narrowing the job. One agent decides which pages matter. One agent recovers the table structure. One agent extracts values exactly as they appear. One agent preserves provenance, so outputs can be traced back. And the most interesting step: an agent writes Python code to map the extracted table into the user’s schema. That last part matters a lot. Because once mapping becomes code, it becomes deterministic. “Cur SEC” does not need to be reinterpreted on every row. “1,105.00” does not need to be renormalized by an LLM every time. A customer-specific table does not need to be reasoned through from scratch on every document. The model reasons once. The system reuses the decision. That is the shift. My co-founder Arun Venkataswamy wrote up the full architecture, with real examples of the messy tables that forced us into this design. I’m sharing it in the first comment. If you deal with rent rolls, bank statements, SOVs, or any document-heavy workflow at volume, I suspect parts of this will feel very familiar. Unstract

Next up! if you’re developing or integrating with AI agents right now, you’ve probably got a dozen + MCP servers running like I do and zero idea which ones are actually alive and healthy. We’ve all done it (I know i have) added a search tool or data source “just to try it” six weeks ago and forgot about it, ended up with three integrations doing basically the same thing. No way to see this all together one place. You’re piecing it together from memory, a stale README and maybe your browser history. This was a massive problem while building Apollo, dozens of tools and code sources and I wasn’t game enough to entangle it all. That painful experience gave rise to designing Atlas. Think of it as the SaaS-license-audit idea (the Zylo/Productiv vibe), but for the tools your agents actually call and rely on for operations condensed into a live map of everything you’re running locally and offsite, a plain-language health check, and straight forward recommendations when things overlap or go stale that your not going to notice when your spread across 10+ projects. It sits on top of what you’ve already built so nothing to migrate, nothing to lock into. Built for solo devs and small teams who don’t have the big enterprise budget to be “buying” over “building”. Early days in pre-Alpha but looking forward to having something ship-able before the end of 26’ 🥳