\# SafeAgent — Deck v1



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\# Slide 1 — Title



\# SafeAgent

\## The Execution Control Layer for AI Agents



The trust boundary between model decisions and irreversible real-world actions.



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\# Slide 2 — The Problem



\## AI agents can now act.



They can:



\- send payments

\- place trades

\- update systems

\- trigger workflows

\- call APIs

\- message customers



Once agents cross into action, failures are no longer harmless.



They become:



\- duplicate execution

\- stale decisions

\- incorrect replay

\- partial completion errors

\- irreversible side effects



The industry has focused heavily on:

\- model quality

\- memory

\- orchestration

\- tool use



But it is missing a critical layer:



\# execution control



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\# Slide 3 — The Missing Layer



\## Today’s stacks can decide what to do.



They are weak at deciding:



\# whether an action should still be allowed to happen



under:



\- retries

\- timeouts

\- uncertain completion

\- stale context

\- resumed workflows

\- changed world state



This creates a dangerous gap between:



\*\*agent reasoning\*\*

and

\*\*real-world action\*\*



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\# Slide 4 — Core Insight



\# Idempotency ≠ correctness



A system can safely retry an action

and still do the wrong thing.



Because:



\- the world changed

\- the original context went stale

\- part of the workflow already completed

\- the original intent no longer makes sense



The real problem is not just:



\## “did this already happen?”



It is:



\# “should this still happen now?”



That is the missing infrastructure layer.



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\# Slide 5 — The Product



\# SafeAgent



\## An execution control layer for AI agents



SafeAgent sits between:



\*\*agent / workflow / tool call\*\*

and

\*\*real-world action\*\*



Core functions:



\- durable execution identity

\- intent → attempt → result receipts

\- replay control under uncertainty

\- stale / invalid execution detection

\- policy hooks for block / replay / escalate / re-evaluate

\- action traceability and auditability



This turns execution from a blind step into a controlled decision boundary.



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\# Slide 6 — Example Failure Mode



\## Trading Bot Retry Demo



Without execution control:



\- order submitted

\- broker timeout / uncertain completion

\- retry path triggered

\- duplicate order can execute

\- risk / position becomes invalid



With SafeAgent:



\- execution identity persists

\- retry resolves against prior action

\- result is reconciled instead of replayed

\- duplicate is blocked



This same failure pattern exists in:



\- payments

\- email / messaging

\- browser automation

\- customer workflows

\- API mutations

\- ticketing / ops systems



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\# Slide 7 — Why This Matters Now



\## AI is moving from chat → action



The next generation of AI systems will be:



\- asynchronous

\- long-running

\- side-effecting

\- tool-using

\- autonomous

\- multi-step



As this happens, every serious system will need:



\- execution traceability

\- replay control

\- decision validation

\- action governance



SafeAgent is designed to become that layer.



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\# Slide 8 — Commercial Direction



\## SafeAgent can evolve into:



\- SDK / runtime for agent systems

\- execution layer embedded in workflow platforms

\- policy engine for AI actions

\- enterprise control plane for agent-side effects

\- trust / governance layer for autonomous systems



Potential paths:



\- platform adoption

\- framework integrations

\- enterprise pilots

\- strategic partnerships

\- acquisition by AI / infra / workflow company



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\# Slide 9 — Bottom Line



\# AI systems can already decide what to do.



They are not yet reliable at deciding:



\# whether they should still do it.



\## SafeAgent exists to solve that gap.

