AEGIS: Evidence-Backed Control Layer Reducing AI X-Risk

An inline authorization platform for AI agent actions

Project Details

We are building the inline layer that stands between AI agents and the real systems they act on. Every tool call or API action an agent attempts passes through AgentShield (the open-source gateway), gets a real-time verdict from Tenure (the decision engine) allow, block, or gate and is recorded as tamper-evident proof under OAD, the Open Agent Dossier standard.

I am leading as sole technical founder - architecture, product, and the raise grant. My Wife owns the ML workstream, the ARA engine that powers the decisioning and generalizes across dossier verticals. That's the core team: a two-person operation with deep security and ML depth behind it.

This solution will provide cryptographical receipts, generated at the exact moment of each decision, verdict-labeled and chained so any action can be verified end to end. Not a log you hope is accurate - unforgeable evidence of what was decided, inline. In production today: a 62-agent fleet with 9 governed end to end, ~12,000 enforcement cases, zero false-allows, 10,241 chain-verified receipts, and 76 controls mapped across 7 frameworks.

Theory of Impact

This Solution sits in the AI control layer, not alignment the bet that even imperfectly-aligned agents can be constrained in what they do. Most catastrophe paths route through an agent taking a real action: escalating privilege, acquiring resources, doing something irreversible. It gates those inline checks and leaves tamper-evident, signed receipts a compromised agent can't rewrite making misbehavior detectable and actions reversible-by-default. OAD as an open standard pushes ecosystem-wide accountability norms.

The honest boundary: this raises the floor for near-term agentic risk. It doesn't solve alignment against a superhuman adversary, an inline gateway is a speed bump, not a wall.

People

Vikas Prakash

Team Member

Funding Details

Start Date: Jul 3, 2026
End Date: Jul 31, 2026
Expected Duration: -
Funding Raised to Date: -
Annual Budget: -
Monthly Burn Rate: -
Current Runway: -
Funding Goal: -
Funding Stage: seeking first grant
Fiscal Sponsor: -

Grants Received– no grants recorded

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Funding Asks

grantmaking.ai Launch Round

AEGIS: Evidence-Backed Control Layer Reducing AI X-Risk through Inline

Applied

An inline authorization and cryptographic evidence platform for AI agent actions

Minimum

$50,000

Ideal

$500,000

How the money will be spent

At $500K, here's a clean four-way split:

Bucket | Amount |% |What it covers

Engineering |$175,000 |35% | Founder stipend + fractional/contract engineer, dev tooling, ARA engine build

Design-Partner & Founder sales hire | $175,000 | 35% | One GTM hire, ~6 months fully loaded (salary + payroll + benefits)

Cloud deployment| $100,000 | 20% | Cloud AWS staging + prod, compute, object storage, egress

Infrastructure tooling| $50,000 | 10% | KMS/Vault, CI/CD, monitoring, signing infra, security tooling

Total | $500,000 | 100%

Implied burn ~$28–30K/month → roughly 6–7 months of runway.

One honest flag: $500K is a bridge, not a milestone round it keeps us shipping and lands maybe 1–2 design partners, but won't reach the 3-partner / SOC 2 / seed-ready bar. The GTM hire is the tightest assumption; at this level it likely has to be part-time or a ~6-month ramp, not a full-year FTE.

Discussion

Faw Ali17d

working on something similar :)