The operating view for AI-assisted work

Know where AI is working. Decide what changes. Prove the result.

Harnexis gives leaders and operating teams one shared view of AI-assisted engineering work: what needs attention, who owns the next decision, where material exposure remains, and what previous decisions actually changed. The technical evidence stays one click away.

START WITH READ-ONLY GITHUB EVIDENCE · NO SAMPLE DATA IN THE LIVE PRODUCT · EVERY CLAIM SHOWS ITS BOUNDARY
  AI OPERATING BRIEF · ILLUSTRATIVE
NEXTDeveloper Experience owns one scoped review decision
CHANGEreduce dependency-update review from 100% to 20%
EXPECTEDapproximately 120 fewer review decisions per quarter
BOUNDARYcritical and production repositories remain reviewed
OBSERVEDprior docs change removed 147 decisions · 1 caught before merge
DECISION READY owner · scope · expected effect · safety boundary
technical evidence available on demand
Evidence sources and enforcement points GitHub Slack Datadog Prometheus PagerDuty Argo CD HashiCorp Vault Entra Agent ID ServiceNow
Why Harnexis

AI work is spreading faster than accountability.

Coding assistants now invoke subagents, open pull requests, diagnose failures, and recommend production changes. Usage dashboards show activity. Prompt files describe intent. Neither tells an operating team what to do next or proves what its decisions changed.

WHERE
is AI-assisted work actually happening?

See enrolled work by accountable workflow, repository, harness, credential, and action class without inventing child-agent identities from content.

DECIDE
what should change, and who owns it?

Turn observed evidence into a scoped action with a named owner, expected effect, safety boundary, and measurement window.

PROVE
what happened after the decision?

Keep expected impact separate from observed results, including verified quality, evidence gaps, demotions, and protection outcomes.

The product

From activity to action, without hiding the proof.

The first screen is deliberately plain. Underneath it, Harnexis preserves the append-only records, policy versions, provenance, verified outcomes, and authority observations technical teams need to trust the conclusion.

01 · OBSERVE

Start with real history

Import read-only GitHub pull-request, review, and CI evidence. No new approval inbox is required to see the first posture.

02 · EXPLAIN

One operating brief

Show where AI work appears, what evidence is missing, and which scoped action a named operating team should consider next.

03 · BOUND

Change only within limits

Customer-owned policies define scope and thresholds. Authority evidence shows whether destructive capabilities remain available outside the model.

04 · MEASURE

Show what changed

Compare the expected effect with observed post-decision evidence. Every simplified statement resolves to technical records.

A safeguard people immediately understand

The prompt says “ask first.” The credential still says DROP.

Instructions tell an agent what it should do. They do not remove what its credential can do. Harnexis makes that gap visible, helps prepare a customer-owned least-privilege correction, and records how and when the resulting boundary was observed.

Declared only

A rule the agent must remember

CLAUDE.md says to confirm destructive actions. The production-debug credential still owns the database.

database.dropAVAILABLE
table.truncateAVAILABLE
role.assume-ownerAVAILABLE
Authority attested

A boundary enforced outside the model

The customer applies least-privilege database or Vault configuration. Harnexis reads effective authority back, then the customer can challenge the same artifact safely against a disposable clone.

schema.inspectALLOWED
database.dropUNAVAILABLE
execution modePROPOSE-ONLY
evidence strengthCHALLENGE-TESTED
observed at12:00 UTC
expires at13:00 UTC

Scoped assurance, never magic: every claim names the agent identity, action class, environment, enforcement points, observation time, expiry, maximum drift-detection interval, and evidence strength. Point-in-time evidence does not claim continuous or execution-time protection. Unknown credentials or bypass paths produce a critical finding — never a green badge.

How an approved decision becomes a control

More autonomy is earned in small, reversible steps.

Technical teams can inspect the underlying tier, policy threshold, action class, blast radius, verification record, demotion trigger, and authority boundary. Executives do not need to learn those objects to understand the decision.

TIER 1 · SUPERVISED

Every action reviewed

The default for every new agent and action class. Humans see and approve each proposal; every verdict feeds the ledger.

EARN TIER 2 → clean approval streak · zero verified failures · policy thresholds met
TIER 2 · SPOT-CHECKED

Sampled oversight

The agent acts; humans review a policy-defined sample. Verified outcomes keep the trust score honest between checks.

EARN TIER 3 → sustained verified record at sample rate · blast-radius history clean
TIER 3 · AUTONOMOUS

Within bounds

Unsupervised inside an explicit blast radius. One verified failure and the circuit breaker demotes the agent — automatically, in seconds.

ALWAYS → outcome verification on · demotion trigger armed · every action ledgered
Who uses Harnexis

One shared product. Different operating decisions.

CTO · VP Platform

Know what needs attention

See where enrolled AI work appears, what material evidence gaps remain, which operating team owns the next decision, and what prior decisions changed.

Platform · Developer Experience

Own the scoped change

Move from a noisy estate to a concrete action with scope, expected effect, safety boundary, measurement window, and technical evidence.

Security

See permission exposure

Prompt rules receive no security credit. Inspect which destructive capabilities remain available, how strong the latest evidence is, when it expires, and what identities are uncovered.

Risk · Compliance · Audit

Reconstruct the conclusion

Follow a simplified statement back to the action, human verdict, policy version, provenance, verified outcome, and authority observation that support it.

Why now

Coding assistants gained subagents. Accountability did not scale with them.

NOW
AI work already spans multiple harnesses

Claude Code, Codex, OpenCode, service identities, and subagents create one operating problem even when durable child identity is unavailable.

OOPS
one destructive action can be unrecoverable

A circuit breaker protects the next action. It cannot restore a deleted database; destructive authority must be removed at the enforcement point before an incident.

PROOF
leaders need results, not another activity chart

A decision should name its owner and expected effect, then return with observed quality, rework, protection outcome, and an honest evidence boundary.

Trust, computed — not vibes.
DETERMINISTIC CHECKS AGAINST REAL SYSTEM STATE · NEVER LLM SELF-ASSESSMENT
Start here

Start with your GitHub history. Leave with an operating brief.

The read-only import shows where enrolled AI-assisted work appears, which review and outcome evidence exists, and where the strongest gaps remain. Design partners can then add policy-backed decisions and a scoped authority-assurance pilot without giving Harnexis a production owner credential.

NOW ACCEPTING 2–3 DESIGN PARTNERS RUNNING CODING OR SRE AGENTS IN PRODUCTION