Agentic Automation

We build agents that do work against your files, systems, and shell — autonomously, but governed. The agent runs unattended, yet every consequential tool call is screened before and after it runs, and risky operations pause for a human "yes." Safe autonomy on a runtime you own — agents that act, not chatbots that talk.

Coming into form an autonomous agent pausing for human approval on a risky tool call, then proceeding or being denied

Coming into form responsible-AI proof — every tool call screened, risky ops pause for a human, real code not a framework brand

Outcomes

An agent that acts — reads, writes, edits, searches, runs commands — through a typed, schema-validated tool surface
Governed autonomy — a guardrails pipeline screens every tool call, and a human-approval gate blocks anything irreversible
Long runs stay coherent and affordable — a context-compaction engine folds completed turns so a tool-heavy run keeps fitting the model's context window
Agents that consume the tools you already run as MCP servers, with human approval where it matters

How it works

An agentic automation is an agent that actually does the work — it reads and writes your files, queries your systems, and runs commands — instead of just describing what it would do. We build that agent: a tool-execution loop over a typed toolset, where the model proposes a tool call, the call executes, the result comes back, and the loop continues until the job is done. The file tools write to a branch-versioned store, so every change is diffable and revertible rather than a raw overwrite on disk.

Autonomy without governance is a liability, so the governance is built in, not bolted on. Every consequential tool call passes through a guardrails pipeline: an input screen before the call, a human-approval gate for anything risky or irreversible, and an output screen after. The verdict is three-state — pass, block, or escalate-to-a-human — and it is wired straight into the loop, so a destructive command is caught before it runs and a side-effecting tool fails closed if a person says no.

We build it through the D2D Line: FRAME the process and where a human must stay in the loop, DESIGN the toolset and the guard policy, BUILD it test-first, VERIFY the block-and-approve paths run green, and SHIP it to you with its tests.

Guardrails are deterministic — and we say so

Our guards are deterministic, rule-based screens, not machine-learning content classifiers. They catch the catastrophic by rule — a destructive filesystem path, a dangerous shell command, a command-injection attempt — and they escalate the risky-but-legitimate to a person. That is a feature, not a limitation: a deterministic guard is one you can read, test, and trust, with adversarial test data behind it. We do not claim ML-based content safety, because that is not what we built.

What you own at the end

The agent, the toolset, the guard policy, and the test suite are yours — source and all. It runs on your infrastructure on a provider-agnostic runtime, and it can consume the tools you already expose as MCP servers. There is no OPST runtime your automation depends on.

FAQ

Can the agent run completely unattended? It runs unattended for everything that is safe, and pauses for a human only where you decide a human belongs — an irreversible or consequential action. You set where that line is in the FRAME phase.

Will a long-running agent blow its context window? That is what the context-compaction engine handles: it folds completed turns non-destructively and summarizes history, so a long, tool-heavy run keeps fitting the window and stays affordable.

How is it priced? Fixed-scope, fixed-fee. The process, the toolset, and the approval points are pinned in the FRAME phase, so the scope is bounded before any build starts.

Coming into form the agent loop with the input-gate, human-approval, output-gate guardrail screening

Proof

A tool-execution loop over a built toolset — read, write, edit, search, run a command, fetch the web, delegate a subtask — with file tools that write to a branch-versioned store; the file-tool integration tests run green against the real data layer.
A guardrails pipeline fused to a human-approval gate, wired straight into the tool-execution loop — input screen, then approval, then output screen — with a three-state pass / block / escalate verdict; the guard suite and the block-path integration test run green.
A side-effecting tool blocks on a human 'yes' before it runs and fails closed on denial — proven by green integration cases, with node-failure escalation running end to end through the real engine.
A graph-native context-compaction engine — non-destructive per-turn folding plus a history-summarization safety net — wired into the agent runtime, with a live visualizer that prints per-turn compression.