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Open the engineering workbenchAutonomy fails at the boundary — where an agent decides, where it reaches for data, where it was taught what "good" looks like, and what it is shown. Nessco covers all four.
A policy boundary that sits between an agent and everything it can touch. Every intent is classified, scored and either allowed, held for a human, or killed — before the tool call leaves the process.
Agents are a new attack surface and a new exfiltration path at the same time. We watch both directions: what gets into the context window, and what leaves it.
Small models that run on your hardware, trained on your data, aligned to your policy — so the safe behaviour is baked in before the firewall ever has to catch it.
An agent that can see is an agent that can be shown something. We inspect what a model looks at — screens, camera feeds, scanned documents — and run the classifiers that have to answer in milliseconds, on the device, without a round trip.
Text inputs get inspected as a matter of course. The moment an agent takes a screenshot, opens a scanned invoice or reads a camera frame, the same untrusted content arrives through a channel most guardrails never look at.
Instructions rendered into an image are invisible to a text filter and perfectly legible to a vision-language model. We read what the model reads — rendered text, QR codes, overlays — and apply the same policy to it as to any other untrusted source.
A classifier on the hot path has a millisecond budget, not a second one. These run quantised on the device, so a camera feed is judged where it is captured rather than shipped somewhere to be judged.
Faces, badges, whiteboards and open screens are masked at the frame, before anything reaches a log, a prompt or a training set. What was never captured cannot leak later.
Vision models decay quietly as lighting, hardware and scenes change. A held-out labelled set runs on a schedule, and a classifier that loses ground is flagged rather than trusted.
The harness wraps the agent loop itself. Nothing reaches a tool, a dataset or the network without clearing all four.
The runtime shim sits inside the agent loop. Every planned tool call, retrieval and network request is captured as a structured intent instead of executing straight away.
A small, fast classifier scores the intent against policy: who is asking, what it touches, how reversible it is, and whether the context that produced it can be trusted.
Allow, redact, hold for a human, or block and terminate. Enforcement happens inline, so a rogue action never reaches the system it was aimed at.
Every decision is signed, stored and replayable. Blocked patterns become training data for your edge models and new rules for the fleet.
Traditional security assumes a human on the keyboard. An agent has credentials, a plan, and no hesitation — it will chain forty tool calls before anyone opens a dashboard. The agentic firewall makes that loop reviewable at machine speed.
Rules are written against what the agent is trying to do — "no writes to production billing", "no outbound POST with customer records" — not against brittle string matches.
Retrieved documents, web pages and tool output are treated as untrusted input. Instructions smuggled into content never gain the caller's privileges.
Per-agent budgets on spend, rows touched, files written and external calls. Breach the cap and the harness freezes the agent with its state intact for review.
agent: support-copilot
boundary:
allow:
- tool: crm.read
scope: "tenant:self"
- tool: ticket.write
limit: 50/hour
hold:
- tool: refund.issue
when: amount > 250
approver: finance-oncall
block:
- sendsTo: "*"
carrying: [secret, pii, source_code]
- origin: untrusted_content
escalates_to: [shell, iam, deploy]
on_violation: freeze + snapshot + pageAn agent connected to your stack is both a target and a courier. Nessco inspects the inbound context that shapes its behaviour and the outbound payload that carries your data.
Poisoned documents, hostile MCP servers, compromised tool responses and supply-chain prompts are quarantined before they reach the model's context.
Everything the agent sends out is classified for secrets, credentials, regulated data and proprietary code, then redacted or blocked according to the destination's trust tier.
One agent probing is noise. Nine agents probing the same boundary in an hour is an incident — correlation runs across the whole fleet, not per session.
Enforcement is the floor, not the ceiling. We train the models that run inside the harness — compact enough for a factory gateway or a handset, aligned tightly enough that the firewall rarely has to intervene.
Frontier-model behaviour distilled into task-specific models you own, so latency, cost and data residency stop being negotiable trade-offs.
The policy that drives your firewall becomes the reward signal in training. Guardrail and model agree instead of fighting each other at runtime.
Red-team suites, refusal benchmarks and behavioural regression gates run in CI. A model that loses ground on safety does not ship.
Deploy as an SDK inside the agent, a sidecar next to it, or a gateway in front of the fleet. Same policy, same audit trail, wherever it runs.
Outbound control, destination trust tiers, DNS and payload inspection for everything the agent tries to reach.
Classification, redaction and residency rules applied to every record entering or leaving the context window.
Capability scoping per agent and per session: which tools exist, with what arguments, under what budget.
The model's plan itself — classified, risk-scored and checked against policy before a single call is made.
Drop-in wrapper for Python and TypeScript agent frameworks.
Process-local proxy for containerised fleets and Kubernetes.
Central chokepoint for model, tool and MCP traffic.
Air-gapped build for on-prem, industrial and offline devices.
Agents with ledger access, held to approval thresholds and full replayable audit.
Clinical copilots that cannot leak a record, running on-prem where residency demands it.
Edge models on isolated networks, with hard caps on anything that touches control systems.
Air-gapped deployment, signed artefacts, no data leaving the perimeter. Ever.
Coding agents kept out of production secrets, credentials and customer databases.
Gateway-class models on the factory floor, trained on your process data offline.
A control boundary that wraps an autonomous agent's execution loop. The agent still reasons and plans freely; the harness decides what is allowed to actually happen. Think of it as the difference between trusting a driver and fitting the car with brakes, limiters and a black box.
Those inspect traffic. We inspect intent. A WAF sees a well-formed API call and lets it through; the harness sees that a support agent — which has never issued a refund above $250 — is about to move $40,000 after reading an untrusted email, and holds it.
Enforcement runs inline on small, local classifiers rather than a round trip to a frontier model. Low-risk intents clear on the fast path; only genuinely ambiguous ones take the deeper review, and only high-blast-radius ones wait on a human.
No. The harness is designed to run entirely inside your perimeter, including fully air-gapped. Training engagements happen on your infrastructure or an isolated environment you control, and the resulting model weights are yours.
Anything that speaks tool calls. The SDK wraps common Python and TypeScript frameworks directly, the gateway sits in front of model and MCP traffic for everything else, and custom loops integrate through the intercept API.
Every decision is logged with the full intent, the matching rule and the context that produced it. You can replay the moment, adjust the policy, and — where it makes sense — feed the correction back into the next training round.
Tell us what your agents can reach today. We will map the blast radius, show you where the boundary should sit, and book you a free consultation with one of our experts — no payment details, no obligation.
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