We build the AI agents your business, security, compliance, and operations teams will actually trust to run in production.

From security and compliance to operations, finance, and the lines of business, we design and build agents across every team and workflow that needs them. Engineered against your controls from the first line of code, owned by your team on day one, and backed by an accountable owner and runbook from ours.

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Why This Matters

An agent loose in your environment with the access of a senior engineer and the judgment of a stranger is not a productivity story. It's a breach narrative your board will read in the postmortem.

The market is flooded with agents that demo beautifully but lack the observability and traceability required for professional operations. These unmanaged tools introduce immediate security risks because they aren't built to withstand the rigors of a modern identity stack or complex data classifications. Without a transparent audit trail and resilient architecture, a generic agent becomes a business liability the moment it touches regulated data or internal change-control processes.

We believe every AI agent should be managed with the same rigor as a high-access employee. This means defining its specific role, mapping its access through least-privilege permissions, and ensuring every output is verifiable. By engineering governance into the architecture rather than bolting it on after a security review, we create agents that are observable and audit-ready by design.

Whether we hand these tools over to your team or provide oversight through our Managed Agentic Services, we focus on long-term resilience over generic automation. This disciplined approach ensures your AI strategy remains as secure and predictable as the rest of your organization well beyond go-live.

40%

of enterprise applications will embed task-specific AI agents by end of 2026

Gartner, 2026 — up from less than 5% in 2025.

By The Numbers

26-31%

average cost reduction reported across supply chain, finance, and customer operations functions running agents in production

Cross-study aggregate, 2025–2026 enterprise AI ROI research.

88%

of enterprises reported a confirmed or suspected AI agent security incident in the last year — the gap our build process is designed to close

Gravitee, State of AI Agent Security 2026 (n=919).

The teams we build for aren't exploring AI, they've tried and hit a wall.

Who This Is For

Situation 1

Pilot stalled at the security review

You built something promising, but it hit a wall at the security review. Your team found no identity model, no data boundary, and zero observability into how decisions are made. Because the project lacks the traceability and resilience required for your stack, it hasn't moved in two months.

Situation 2

A specific workflow eats your team's week

Tier-1 alert triage, vendor intake, compliance evidence collection, you know which workflow is draining your hours. You want an agent that owns it, not another dashboard to monitor.

Situation 3

Regulated industry and off-the-shelf doesn't fit

The vendor agents assume your data can leave your boundary. It can't. You need something built inside your constraints, not retrofitted to them.

Best Outcome

A production-ready agent with security signed off on the architecture. You get a resilient, observable system with full traceability and enforced data boundaries from day one.

Best Outcome

A managed, domain-specific agent running that workflow from start to finish with your privacy and policy enforced at every step.

Best Outcome

An agent architecture aligned with NIST RMF, ISO 42001, and your industry-specific frameworks from day one.

You end with a set of managed production agents your team can explain, extend, and defend.

Every agent and artifact is documented, managed, and maintained. You retain full visibility and control over the system, ensuring it remains observable and audit-ready throughout its lifecycle.

What's Included

After this engagement, you will have:

A domain-specific agent running in production

Built against your workflow, deployed in your environment, integrated with your identity and logging stack, not a sandbox demo.

After this engagement, you will have:

A multi-agent orchestration layer

When the workflow needs more than one agent, a documented orchestration layer with deterministic routing, retries, and human-in-the-loop gates.

After this engagement, you will have:

Agent identity and least-privilege access

Every agent has a scoped service identity, a rotation policy, and permissions reviewed against the principle of least privilege.

After this engagement, you will have:

An evaluation harness and red-team suite

Regression tests, adversarial prompts, and drift checks, runnable on every change so regressions don't ship to production.

After this engagement, you will have:

A runbook and named owner for each agent

A fully documented environment with clear runbooks. You receive comprehensive guides explaining exactly how the agent works, including escalation paths and kill switches, to ensure long-term stability and oversight of the system.

After this engagement, you will have:

API integrations into your existing systems

Enterprise-grade connections for service management, ticketing, and notification systems. We use secure APIs and contracts that survive change management, rather than brittle screen-scrapers that break on UI updates.

Four phases from signed scope to production ownership.

How It Works

Phase 1

Scope & guardrails

We define exactly what the agent does, what it can't, and which data and actions require human approval before any code is written.

Phase 2

Design & build

We design the agent architecture, orchestration flow, and evaluation harness and build iteratively with your stakeholders in every review.

Phase 3

Secure & integrate

We stand up agent identity, logging, rate limits, and the real integrations into your production systems with your security team reviewing each gate.

You Walk Away With

Signed scope & guardrail spec
Data-boundary definition
Human-in-the-loop policy

You walk away with

Architecture doc
Agent + orchestration code
Eval + red-team harness

You walk away with

Scoped identities deployed
Telemetry pipeline live
Production integrations signed off

Phase 4

Handoff & enable

We train your engineers, publish the runbook, and stay alongside for a supported cutover before you fully own it.

You walk away with

Runbook + escalation tree
Trained engineers on your team
Quarterly review cadence set

Differentiator · forward-deployed engineering

We deploy engineers into your environment; not account managers who forward tickets.

The agents that survive production are the ones built inside the constraints of the team that will operate them. Our model is forward-deployed: senior engineers work inside your environment, alongside your security and platform teams, until the agent is theirs, not ours.