Hamed Taheri

The best models fail 30% of the time on complex tool-calling scenarios. Seven documented error patterns, infinite loop failures, and silent cascading errors. Here's what the data says before you ship function calling to production.

Delivering the right context to agents is one problem. Ensuring they respect what they must never do is another. Here's how we designed our adversarial governance experiment, what our results show, and why this work is never finished.

38% of valuable information no schema anticipated. 12% only usable with type enforcement. Neither modality alone captures the full picture, and both come from one extraction pass.

Dual memory, governance routing, reflection-bounded retrieval, and schema lifecycle — the architecture we built when RAG wasn't enough.

Sales embeds brand voice in a system prompt. Support copies compliance rules from a Notion doc. Marketing uses its own tone guidelines. When legal updates the data handling policy, nothing propagates.

When agents re-plan and act in loops, re-injecting the same governance context at every step is expensive and makes outputs worse. Here's the fix.

When the information an agent needs is scattered across 3–5 sources, a single retrieval pass misses most of it. Here's what actually works — and the surprising finding about what drives the gain.

Free-text memories can go into a prompt. They can't sync to Salesforce, filter by deal stage, or aggregate across 10,000 entities. That's the downstream dead end.

Schemas age. Models get updated. Content types shift. New agent workflows produce data the schema wasn't designed for. Here's how to build a schema that keeps up.

Output quality saturates at roughly seven governed memories per entity. More context isn't better context — it's expensive noise.

Your enrichment agent knows the CTO is evaluating three vendors. Your outbound agent sends a generic cold email anyway. This is how memory silos cost you deals.

Most redaction pipelines scrub PII from the output. We scrub it before the LLM sees the content and again after extraction. Here's why both phases are necessary.

Transparent breakdown of our experimental results: what we tested, what the numbers prove, what they don't, and why we benchmark against ourselves honestly.

100 entities, overlapping names, same industry, similar roles — and zero actual memory bleed. Here's how entity isolation works when embeddings can't save you.

Every workflow learns something. No workflow shares it. This is where organizational intelligence goes to die.

I installed our own SDK as a customer with a standard API key. No internal shortcuts. This is what I built and what happened.

Three questions that reveal whether your AI agents have what they need — or whether you're building on gaps.

A response to The New Stack's excellent taxonomy. They got six right. Here's the pattern nobody's building yet, and a practical blueprint for how to build it.

Google, Microsoft, and Salesforce are each solving a piece of the agent governance puzzle. Here's what the pattern reveals — and the gap nobody has closed.

The biggest companies in tech are converging on the same conclusion: AI agents without organizational knowledge are a liability.

Same company, same task, three different AI agents, three completely different answers. Your customers notice. Do you?

The gap between 'stored' and 'remembered' is where agent quality lives.

If your AI agents forget everything between conversations, they're not agents — they're expensive autocomplete.