ThoughtShieldAI
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The AI control plane for enterprise agentic AI

Every AI agent in your enterprise, on one screen.

ThoughtShieldAI finds every agent running across your clouds, maps which agent calls which, and shows what each one costs. No SDK, no code changes, and nothing leaves your infrastructure.

Run a pilot on your fleet See how it works
01

Complete AI visibility

One trusted inventory of every AI agent, model, tool, and MCP server across every cloud and business unit.

02

Shadow AI surfaced

Agents running outside the approved inventory are named, owned, and put in front of security review.

03

AI spend under control

Token usage, model cost, and duplicated agents attributed back to the agent and the team that caused them.

Connects to
Claude Code Codex Amazon Bedrock Google Vertex AI Azure AI Foundry Cloudflare MCP

Your agent count went up
again last week

Nobody approved it, nobody logged it, and nobody owns it. Here is what that costs you.

01 Nobody has the list Agents pile up across clouds and teams with no inventory and no named owner.
02 Shadow AI ships anyway Anything deployed outside approved channels is invisible to security review and to audit.
03 The wiring is a guess No view of how agents, tools, APIs, MCP servers, and data actually reach each other.
04 The bill has no owner Token usage, model costs, and duplicated agents grow with nothing attributing them back.
05 Failures stay quiet A scheduled agent stops running and you find out from the work it did not do.
The platform

Five capabilities,
one control plane

Vendor agnostic across AWS, Azure, Google Cloud, OpenAI, Anthropic, and MCP.

Discover

Every agent, model, tool, and MCP server found automatically. No SDK to install, no code to change.

Map

Which agent spawned which, what each one reaches, and how much context moved between them.

Observe

Health, usage, and behaviour tracked continuously across every platform.

Govern

Ownership, policy, and compliance held to one standard instead of per team.

Optimize

Cost per agent and per model, counted honestly and attributed back.

How it works

Connect, discover, analyze,
map, visualize, enable

Read only connectors in, one correlated graph in the middle, three surfaces out. Every part of it runs on infrastructure you control.

  1. 01 Connect

    To enterprise AI platforms and agentic AI frameworks.

  2. 02 Discover

    Every AI agent, model, tool, and MCP server, automatically.

  3. 03 Analyze

    Build a centralized AI inventory and usage profile.

  4. 04 Map

    Agent relationships, dependencies, and end to end lineage.

  5. 05 Visualize

    Inventory, token usage, cost, and governance insight.

  6. 06 Enable

    Security, governance, and FinOps on one AI inventory.

Enterprise AI control plane architecture
Enterprise AI ecosystem OpenAIClaudeGeminiAzure AIAmazon BedrockVertex AI LangChainMCP servers and toolsCustom and internal agents
ThoughtShieldAI Enterprise agentic AI control plane Discover · Secure · Govern · Observe · Optimize
Shipped ATLAS AI discovery and inventory
  • Discover AI agents
  • Enterprise inventory
  • Relationship mapping
Shipped VISION AI observability
  • Runtime monitoring
  • Subagent health
  • Silent failure alerts
Partial PULSE AI FinOps
  • Token and usage analytics
  • Cost per agent and model
  • Vendor wide FinOps building
Building PHOENIX AI security
  • Threat detection
  • Data protection
  • Risk prioritization
Building NEXUS AI governance
  • Trust and identity
  • Policy enforcement
  • Compliance monitoring
Unified telemetry and data layer Cloud platformsAPIs and servicesMCP servers and tools Databases and data storesCode reposLogs and eventsIdentity and access

Ingestion is strictly read only. Data lands in a local SQLite store on your own infrastructure, behind one shared query layer, so the CLI, the dashboard, and the MCP tools report identical numbers instead of three drifting answers.

Complete AI visibilityKnow every agent, model, and relationship. Trusted AI operationsReduce risk and protect data. Optimized AI investmentLower cost and maximize return. Enterprise scaleBuilt for security and performance.

ATLAS, VISION, and PULSE are running in the product today. PHOENIX and NEXUS are in active development and their sequencing is genuinely open. Underneath all five, no connector can mutate a source platform: Vertex, Bedrock, Cloudflare, and AI Foundry are catalog only reads, and we treat any code path that could write back as a security bug rather than a feature request.

This is the running product

Screenshots of ATLAS and the relationship graph as they ship today, not concept mockups.

Atlas

One inventory that stays current

Atlas connects to each platform read only and builds the inventory itself, then keeps it current as agents appear, change, and retire.

  • Reads the logs your agents already write
  • Captures platform, owner, trigger, and staleness per agent
  • Surfaces agents running outside the approved inventory
ThoughtShieldAI Atlas dashboard listing discovered agents with alerts, stale agents, and estimated cost
Lineage

Trace a workflow instead of guessing at it

When an agent triggers another agent, that relationship is captured automatically, so you can follow a workflow end to end.

  • Agent to agent, agent to tool, and agent to data relationships
  • Volume of context passed along each edge
  • Active against stale subagents, surfaced per lineage root
ThoughtShieldAI Lineage view showing a graph of agents spawning subagents across several platforms

Screenshots show a sample fleet used for demonstration, not a customer environment.

What works today,
and what does not yet

We would rather lose a deal on scope than win one on a roadmap promise.

ShippedWorking today
Zero instrumentation discovery across Claude Code, Codex, OpenClaw, and Hermes
Read only cloud connectors for Vertex, Bedrock, Cloudflare, and Foundry
Agent to agent lineage graph with data sharing metrics per edge
Cost analytics that label what cannot be priced instead of guessing
Silent failure alerts on declared cadences and token spikes
CLI, dashboard, and 16 MCP tools over one shared query layer
BuildingIn active development
Pulse, full FinOps intelligence across every major model vendor
AI native security and threat detection
Risk scoring for shadow, orphaned, and over privileged agents
Compliance tracking against internal AI use policy
Department and team spend rollup
Multi tenant workspaces, RBAC, and SSO

Sequencing is genuinely open. Design partners get to decide what ships first.

Security and trust

Nothing leaves your
infrastructure

ThoughtShieldAI reads transcripts your agents already wrote and keeps every byte on hardware you control.

Loopback only dashboard

Binds to 127.0.0.1. State changing requests need a same origin check and are size capped.

Credentials stay out of the database

Connector credentials live in a local, permission locked config file and never enter the fleet store.

Fails safe on hostile input

Every parse is guarded, so a corrupt or malicious transcript cannot crash or hang the collector.

Watchdogs cannot be quietly disabled

Cadence grammar is bounds checked, so a declared alert window cannot be degraded by a bad interval.

Every connector is strictly read only

No connector can mutate a source platform. Vertex, Bedrock, Cloudflare, and Foundry are catalog only reads, and we treat any path that could write back as a security bug.

Why ThoughtShieldAI

The status quo is not a competitor.
It is tribal knowledge and spreadsheets.

vs doing nothing

Today "who owns this agent" lives in someone's head. ThoughtShieldAI turns that into a queryable inventory that updates itself every time an agent runs.

vs generic observability

APM and logging tools were not built to understand agent to agent spawning. ThoughtShieldAI treats the parent to child relationship as a first class object, not a trace span.

vs platform native consoles

Each platform's own console only sees its own agents. ThoughtShieldAI is the one view that spans Claude Code, Codex, Vertex, Bedrock, and every other platform your teams run.

The relationship graph is the part nobody else ships

Agent to agent lineage, with real data sharing metrics on every edge, is not something we have seen shipped anywhere else at this fidelity.

Partnership and system integrators

An open source core, built to
co-develop the governance roadmap

The core is MIT licensed, so a system integrator can stand up a pilot on a client's own infrastructure without a procurement cycle, read the code that touches client data, and contribute back what a real deployment surfaces.

01 Fast pilots Self hosted on the client's own machine or infrastructure. No vendor procurement gate before a first pilot can start.
02 Auditable by design Your technical reviewers can read every line that touches a client's agent transcripts before it goes near a client environment.
03 Roadmap, together The governance and compliance layer is scoped by real deployments, not a whiteboard. Partner feedback decides what ships next.

We have not fixed a pricing or revenue share model yet, and that is deliberate. We would rather design the engagement model with the first few partners than impose one nobody asked for.

Questions we get asked

The technical review,
answered up front

What is an AI control plane?
An AI control plane is a single system that inventories every AI agent an organization runs, records how those agents call each other and which tools and data they reach, monitors their health, and attributes their cost. ThoughtShieldAI provides this without requiring an SDK inside each agent.
How does ThoughtShieldAI discover AI agents without instrumentation?
It reads the transcripts and logs that agent runtimes already write to disk, and queries read only platform APIs through CLI sessions you are already signed into. No SDK is added to your agents and no application code changes.
Does ThoughtShieldAI send our data to a vendor cloud?
No. It is local first. Data is stored in a SQLite database on your own infrastructure and the dashboard binds to loopback only. Connector credentials stay in a permission locked local config file and never enter the fleet database.
Which platforms does ThoughtShieldAI support?
Local agent runtimes Claude Code, Codex, OpenClaw, and Hermes, plus read only catalog connectors for Google Vertex AI, Amazon Bedrock, Cloudflare, and Azure AI Foundry. It also exposes 16 MCP tools so an assistant can operate the fleet.
How does ThoughtShieldAI find shadow AI agents?
It builds the agent inventory from what is actually running rather than from what was registered, so any agent producing transcripts or appearing in a connected platform catalog shows up even if it was never approved. Agents outside the approved inventory are surfaced with their platform, owner, and last run.
How does ThoughtShieldAI attribute AI agent cost?
It reads token usage per run from the agent transcripts and applies per model pricing to produce cost per agent and per model. Runs it cannot price are labelled as unpriced rather than estimated, so the totals are never inflated by a guess.
Is ThoughtShieldAI open source?
The core is open source under the MIT license and published on GitHub, so a security reviewer can read every line that touches agent transcripts before it runs in a client environment.

See your real fleet in an hour

Point ThoughtShieldAI at your existing agent logs and get a live inventory, a lineage graph, and a cost view. Self hosted, and nothing leaves your infrastructure.

Run a pilot on your fleet Read the source

adarshk@thoughtshieldai.com