June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)
[ Coding Agent Security Platform ]
For AI platform, engineering, and SRE leaders
A sub-millisecond check between your coding agents and the destructive command, designed to fail open: a crash in our layer never blocks a developer. Code review and CI can’t carry this job — the dropped table and the leaked key happen in the session, before any pipeline runs.
[ Why engineering cares ]
You approved the coding agents, you run the gateway and the endpoint fleet, and when an agent does something destructive, the recovery lands on your team. Three realities shape the problem:
[ 01 ]
A cleanup prompt that hits the wrong database. A force-push to a protected branch. The wrong package installed. A key carried into a public commit. No attacker involved, and still your incident.
[ 02 ]
The purchase is security-led, but the gateway, the endpoint management, SSO, and CI are yours. Whatever gets deployed has to respect your stack and your latency budget.
[ 03 ]
New agent brands, new harnesses, a model router behind them. Policy that has to be rebuilt per tool is a tax on every change you ship.
[ The entry ticket ]
[ The reliability win ]
The same chain analysis that serves security catches the operational failures first: leaked secrets, dropped production tables, wrong packages, unreviewed deploys, runaway loops burning compute and tokens — caught before they execute, not found in the postmortem. When the platform flags a near-miss, engineering gets the save.
[ Adversa AI behavior graph ]
A poisoned repo or skill gets installed. The agent ingests it and starts acting on an attacker’s instructions. It reads a secret. It pushes a commit — your API keys inside. No single event was suspicious, but the chain of events became the breach.
The platform builds an agent behavior graph: every step the agent reasons through and every action it takes, with its inputs and effects. Then it scores risk across the chain as it forms, across the model, the tools, and the machine. When the chain crosses the line, it’s blocked before it completes. The developer gets a clear error and keeps working.
[ Proven in production ]
A financial institution routed Codex, Claude Code, and Copilot through the platform: visibility from day one, and developers never noticed it was there. In the first week it stopped a real attack the AI gateway, EDR, and SIEM all missed: an agent absorbed a malicious skill, read internal API secrets, and tried to send them out.
0.2 ms
Median blocking decision, measured in production
Developers don’t feel it.
2,000:1
Raw signals per confirmed incident
No alert flood.
[ Your stack stays yours ]
An AI gateway is for traffic control, not risk management. Yours is the right infrastructure, and it stays; we enrich it with the layers it was never built for. Policies hold when developers switch tools, or when your model router switches providers — self-hosted and API models both.
| Capability | A typical AI gateway | Coding Agent Security Platform |
|---|---|---|
| Routing LLM traffic for models and agents | Yes — keep it | Reads selectively from your gateway, or runs as a standalone gateway |
| Monitoring tool execution and MCP calls, separated from model traffic | LLM traffic only | Yes, logging which systems are touched, which commands run |
| Recognition of what’s actually happening (semantic analysis of the traffic) | No | Yes |
| Combining steps into chains, risk scored progressively as the chain builds | No — each step analyzed separately | Yes — chain risk scoring on the agent behavior graph |
| Endpoint agent runtime monitoring (files, shell, CI/CD) | No | Yes |
| Baseline policies: dangerous events and attacks caught by default | Your team must write and maintain rules | 300+ default policies out of the box, updated by our offense team |
| Enforcement | Prompt-level filters | Warn, deny, or human-in-the-loop at action level |
The hidden cost of homegrown rules is maintenance: techniques against coding agents evolve monthly, and somebody has to keep detection current. Our offense and research teams do it for a living, so it doesn’t become your platform team’s backlog.
[ Autonomy ]
With inline enforcement and a recorded would-have-blocked trail, higher autonomy modes stop being a leap of faith. Security approves more agent use, not less, when the control exists — and the evidence for that conversation comes out of the platform, not out of a meeting.
[ Rollout ]
Rollout is a config push through the endpoint management you already run plus an SSO group — or a manual one-line config change. Start with one team and widen from there; the scope starts small, not the coverage. Observe mode first; blocking per policy, when you decide.
[ Platform architecture ]
Two components, one platform:
Intercepts the agents’ own traffic: LLM calls, responses, tool executions, and MCP calls. Inline, or reading from the LLM/API gateway you already run.
Watches actions on the machine: files, shell, CI/CD. Strictly the agent’s actions, not the developer’s.
Harness hooks and direct reads of the top vendors’ compliance APIs complete the capture. Both components feed one policy engine; the chain is scored as a whole; the response follows your policies: warn, deny, or human-in-the-loop. Cloud or on-prem.
[ Policies ]
Our red team maintains the rulebook: every known coding agent attack technique and public incident, covered. You start protected without writing a thing.
Say “no agent in the dev team ever writes to production servers”. The platform turns the sentence into a precise, enforceable policy. Generated policies are validated, simulated, versioned, and approved before they go live.
Rules match the shape of dangerous behavior, with content as one signal alongside trust state, action type, identity, and provenance. A reworded attack trips the same rule, and policies you write today still hold against techniques invented next year.
[ Trust & Proof ]
Adversa AI experts are co-leads and core members of industry-defining frameworks and initiatives: NIST AI RMF, OWASP ASI, CoSAI, CSA AI CM. Trusted by Fortune 500 enterprises including top banks, insurance companies, fintech, Big Four, and automotive enterprises.
Analyst recognition
Standards leadership
A decade of adversarial research
20+ industry awards
20+ industry awards
[ See it yourself ]
The demo takes 30 minutes. If it fits, a scoped observe-mode pilot follows: one dev team, the agents you’ve approved. Our measured overhead on your own traffic is a written pilot success criterion, next to the would-have-blocked list and its false positive rate.
Form not loading? Open it in a new tab.
The median blocking decision measured 0.2 ms in production; the heaviest paths stay under 50 ms, and the design fails open: a crash never blocks a developer. You measure our overhead on your own traffic as a pilot success criterion.
No. The sensor watches strictly the agent’s actions, not the developer’s, and coverage is scoped to the coding agents your company approved.
No. Your gateway keeps routing; we read from it selectively and add the layers it can’t see: tool executions, endpoint actions, and chain-level analysis with enforcement. No gateway yet? The agent security gateway runs as one.
Keep them. What a gateway rule can’t reach by design: endpoint actions, tool and MCP calls separated from model traffic, and risk scored across the chain rather than per request. And the detections here are maintained by our offense team, not by your backlog.
Yes. The gateway sees the agents’ traffic wherever it goes, and policies hold across the providers a router splits calls over — self-hosted and API models both.
Yes. The sensor covers files, shell, and CI/CD on developer machines and CI runners alike.
The gateway runs inline or reads from the gateway you already have. The endpoint sensor is lightweight, watches AI activity only, and runs beside CrowdStrike-class EDR. Rollout is a config push plus an SSO group.
Response is graduated by policy: warn first, deny, or hold for a human in the loop. The developer always gets a clear error that says which rule fired, not a silent failure.
Yes — pilots start in observe mode: watch, log, and review the would-have-blocked list with its false positive rate on your own traffic. Blocking is per policy, when you decide. Observe-first is the on-ramp, not a ceiling: the first production customer switched prevention on in week one.
The agents you’ve approved — Claude Code, Copilot, Cursor, Codex, and what comes next. Policies hold when developers switch tools, or when your model router switches providers.
Action telemetry, not your codebase. Secrets are never stored; what we ingest is documented in the trust pack, and when we read from your gateway you control what telemetry we receive.
14–30 days, one dev team, observe mode only, success criteria agreed in writing before the start — including measured overhead on your own traffic and the would-have-blocked list that justifies turning blocking on.
