What buyers usually already have
- Models, planners, agents, or orchestration frameworks
- Task-specific autonomy logic and operating constraints
- People responsible for oversight, delivery risk, and auditability
Deployment-Time Assurance Runtime
Complementary Layer
Bounded • Auditable • Recoverable
Make deployed autonomy governable.
Zero-G Engine is the runtime layer between autonomy software and real-world action. It helps teams ship autonomous and agentic systems with stronger control, clearer escalation, usable evidence, and more survivable deployment behavior.
Built for integrators, primes, and platform teams that already have agents, models, or autonomy components and now need a runtime they can explain to customers, operators, and internal reviewers.
For teams shipping agents, autonomy, and mission-critical software into the real world.
206
Adversarial test vectors across the current runtime assurance program
36
Controls mapped through the threat, findings, patch, and regression chain
4
Core governance layers exercised under adversarial conditions
97%
Execution-boundary hardening effectiveness reported in the external-safe summary
Why This Category Exists
Capability is accelerating. Runtime discipline is not.
The hardest deployment problem is no longer just model access or orchestration. It is what happens after a system leaves the lab and enters operational software, mission workflows, and high-consequence environments where action has to be constrained and reviewed.
What Buyers Get
A clearer deployment story without replacing the stack.
The commercial wedge is straightforward: help teams keep the systems they already have, while adding the runtime control and evidence they still need to deploy with confidence.
Stronger customer confidence
Give buyers and reviewers a clearer explanation of how autonomous actions get constrained, escalated, and recorded at deployment time.
Lower delivery risk
Reduce the gap between what the system can do and what your team can credibly stand behind when it is live.
Faster technical diligence
Move proof, oversight, and runtime behavior from vague assurance claims into a review path that technical stakeholders can actually work with.
Runtime Mechanics
One control loop, six design mechanics.
The product should not feel mysterious. The runtime exists to make one live path legible: observe, score, constrain, escalate, record, and recover. That is the operating loop.
01
Observe
Take in decision context, environmental state, and execution intent before action leaves the autonomy layer.
02
Score
Evaluate anomaly, confidence, and contextual risk instead of relying on a simplistic yes-or-no gate.
03
Constrain
Bound execution before consequence when behavior or conditions cross the wrong threshold.
04
Escalate
Make higher-risk or abnormal conditions visible to operators instead of letting automation silently drift.
05
Record
Preserve a replayable decision trail so actions can be reviewed, explained, and defended later.
06
Recover
Shift behavior intentionally under degraded resources or pressure instead of collapsing into guesswork.
Bounded Execution
Constrain action before consequence.
Actions are evaluated against context and thresholds before execution, with graduated response instead of a binary allow-or-block pattern.
The runtime protects the action plane.
Explicit Escalation
Keep operator review available where it matters.
High-risk or abnormal behavior is surfaced for human review before a brittle automation chain turns into an operational problem.
Oversight becomes part of the live path.
Tamper-Evident Provenance
Evidence is part of the runtime, not an afterthought.
Decision records are cryptographically chained so the path to action can be replayed, audited, and defended later.
Action leaves a trail the team can use.
Adaptive Recovery
Degrade intentionally instead of failing open.
Under stress or resource constraints, the runtime changes mode deliberately instead of guessing its way through degraded conditions.
Runtime behavior stays shaped under pressure.
Input Integrity
Do not assume the live path stays clean.
Pattern-based defenses, calibration checks, and anomaly scoring help catch manipulation, drift, and confidence abuse on the live path.
Integrity belongs inside runtime mechanics.
Complementary Insertion
Fit existing stacks instead of replacing them.
The runtime is designed as a portable assurance layer across autonomy stacks, which makes it easier to place and easier to buy.
Strong wedge, believable motion.
Proof Discipline
Show the proof where the runtime is strongest.
The most credible story today is runtime control and evidence. That is why the site leads with bounded execution, escalation logic, provenance, and adaptive recovery, then states the proof boundaries plainly instead of overclaiming.
Assurance spine
Threat model → control matrix → methodology → findings → patch receipts → regression → residual risk
206 adversarial test vectors covering input manipulation, drift, confidence abuse, temporal exploitation, provenance, and execution boundary abuse.
Graduated runtime response with anomaly scoring and thresholded containment rather than brittle yes-or-no logic.
Cryptographically chained decision provenance with replay and audit support on the evidence path.
Execution boundary hardening exercised against injection, exfiltration, credential access, and privilege escalation paths.
What this demonstrates
- Bounded action under runtime control
- Visible escalation logic
- Adaptive degraded operation
- Replayable decision trail
What it does not claim
- Production-grade hardening across every attack surface
- Independent third-party validation
- Comprehensive space-environment resilience
- Readiness for untrusted external deployment
Next move
The detailed proof page is designed for technical reviewers who need the evidence posture quickly without pulling the whole internal package.
Initial Markets
Start where runtime trust is already a buying issue.
The strongest first markets are the ones already carrying deployment risk, oversight pressure, and buyer scrutiny. That is where runtime assurance becomes a product wedge instead of a research idea.
Integrators and primes
Teams that already deploy autonomous and agentic systems, absorb delivery risk, and need a runtime layer they can defend in front of customers and program owners.
Defense and space-adjacent programs
Programs that care about constrained operation, provenance, decision review, and maintaining control when oversight is delayed or degraded.
High-consequence enterprise automation
Operational workflows where model capability alone is not enough and the team still needs bounded behavior, usable evidence, and a believable deployment posture.
How Engagement Starts
Make the next step feel concrete.
Serious buyers do not just want to admire the category. They want to know how an evaluation starts, who should join the call, and what they actually get back after the conversation.
01
Share the system context
Bring the autonomy stack, deployment environment, and the runtime-control problem you are actually trying to solve.
02
Walk the insertion point
We map where the runtime sits, what behaviors matter, and which proof boundaries are relevant to your deployment path.
03
Decide the next review path
You leave with a clearer fit/no-fit view, the right proof artifacts to review, and the next technical step if there is one.
Next Step
Start with a 20-minute runtime briefing.
The first conversation should answer three practical questions fast: where Zero-G Engine sits in the stack, what the current proof posture supports, and whether it meaningfully reduces deployment risk in your system.