Every AI governance effort eventually runs into the same foundational problem: you can't govern what you don't know exists. Building an accurate inventory of AI systems and agents in production is often harder than it sounds, because AI features get built fast by teams moving fast, sometimes with zero central visibility, and documentation lags behind whatever's actually running out there.
Documentation alone will not produce a complete inventory
Relying on architecture diagrams, service catalogs, or documentation requests sent to team leads surfaces the AI systems built with process and documentation in mind. It systematically misses prototypes that quietly reached production, experimental integrations added with no formal review, and features built by a team that never thought to flag their work as "an AI system" worth including in this kind of inventory. Treat documentation-based discovery as a starting point, not a complete answer, and you've set the right expectation from the outset.
Direct conversations with engineering teams surface more than documents
Talking directly with engineering leads and asking specific, concrete questions, "does your service call any external model API," "do you have any feature that generates text or takes action based on a language model's output," surfaces systems documentation-based discovery misses, because a direct conversation lets someone realize mid-sentence, oh right, actually, there's a small feature that does exactly that, which they never thought to mention because it didn't feel significant enough to report on its own.
Technical discovery methods add a verification layer
Beyond conversations, technical discovery, reviewing API gateway logs for calls to known model provider endpoints, scanning dependency manifests for AI-related SDKs and libraries, reviewing cloud billing for AI service usage, can surface systems nobody mentioned in conversation, either because the person you talked to wasn't aware of every integration in their own service, or because the system in question was built by a team you haven't reached yet. This technical layer works as a cross-check against the inventory built through direct outreach, catching gaps either method alone would miss.
Capture the right fields, not just a name
For each identified system, record what it does, what data it accesses, what tools or actions it can take, who owns it, what model or provider it uses, and how it gets reached, internal only, customer-facing, or both. That level of detail is what makes the inventory useful for risk classification and prioritization later, instead of producing a list of names with no way to tell which ones actually deserve urgent attention.
Accept an incomplete first pass and keep building
The first inventory pass will be incomplete, and treating that as an acceptable, expected outcome rather than a failure keeps the effort moving instead of stalling while everyone waits for a perfect result that's never coming. An incomplete inventory built in a reasonable timeframe, actively used to prioritize review of the highest-risk systems found so far, delivers more real risk reduction than a delayed attempt at a comprehensive one that takes six extra months.
Build a process for keeping it current, not just building it once
An inventory that's accurate the moment it's completed and never updated again goes stale within months, as new AI features ship and existing ones change underneath it. Build a lightweight ongoing process instead, a requirement that new AI features register with the inventory as part of launch, periodic re-outreach to engineering teams, continued technical discovery scans, and the inventory stays a living resource rather than a one-time snapshot that governance decisions increasingly lean on despite being quietly out of date.
Use the inventory to drive prioritization, not just to exist
An inventory's value comes from what actually gets done with it: ranking systems by risk based on their data access and action capability, and pointing review and control implementation effort at the highest-risk systems first. An inventory sitting there as a static document, referenced occasionally but never actually driving where governance and security attention goes, has produced visibility without the risk reduction that visibility was supposed to enable in the first place.
Where TELEON fits
TELEON's audit trail, applied through the gateway or supported middleware, can surface AI agent activity flowing through that deployment path, which supports technical discovery for systems already integrated with TELEON and provides ongoing visibility into what those systems are actually doing once inventoried.
Direct outreach to engineering teams, broader technical discovery across systems not yet integrated with any specific runtime tool, and maintaining the inventory as a living process remain organizational efforts that have to be driven by whoever owns AI governance.
A short checklist
- Combine documentation review, direct engineering outreach, and
technical discovery methods, since no single method surfaces every system.
- Capture data access, action capability, ownership, and reach for each
identified system.
- Accept an incomplete first pass and prioritize review based on it
rather than waiting for completeness.
- Build an ongoing process, including registration for new features, to
keep the inventory current.
- Actively use the inventory to drive risk-based prioritization, not just
maintain it as a reference document.
An accurate AI inventory is built through combined discovery methods and maintained through an ongoing process, not produced once through a documentation request and assumed to remain complete as the organization's AI footprint keeps growing.