The problem
Teams want the throughput of agent-driven engineering but cannot put ungoverned agents anywhere near production infrastructure. The risk of an unattended `apply` is unacceptable, so that throughput goes unused.
of AI-generated code failed security tests across 100+ models and 80 coding tasks; a pass rate that stayed flat through March 2026 as coding benchmarks improved.
The approach
Multi-agent LLM orchestration applied to real platform work (Terraform, CDK, CI/CD, Kubernetes), behind machine-verified guardrails and a mandatory production-safety gate. Agents propose; verification and policy decide; a human approves the apply. This site was built and is deployed by exactly that system.
The authority ladder we govern against: Least Agency →
Engagement
Project-based buildout or embedded for a fixed term.
What's delivered
- Governed agent workflow: change proposal → automated verification → policy gate → human approval
- Machine-verified guardrails (type checks, policy-as-code, plan/diff review) wired into CI
- A production-safety gate that blocks any apply lacking evidence
- Runbook + enablement so your team operates the workflow after the engagement
The outcome
Agent-scale delivery velocity on infrastructure your security and platform leads will actually approve.
In practice
What this looks like.
Production-safety gate: agent proposal to recorded apply
SampleThe mandatory gate every agent-proposed infrastructure change passes through before any apply, shown as ordered stages.
- Change proposed (agent) An orchestrated agent drafts the change (Terraform, CDK, or a manifest) against a tracked ticket and emits a machine-readable plan and diff. Nothing touches infrastructure yet. A proposal with no readable plan does not advance.
- Automated verification CI runs type checks, lint, tests, and a plan or synth, plus drift detection. Any failing check, or a plan that does not match the stated intent, halts the change here.
- Policy-as-code gate The plan is evaluated against policy-as-code: no public exposure, no long-lived keys, required tagging, blast-radius limits. Any violation is a hard stop with no in-pipeline override.
- Human approves apply A reviewer sees the verified plan, the policy result, and the diff in one view and approves. Apply cannot run without a recorded approval bound to that exact plan; a re-planned change invalidates it.
- Apply executes (scoped) Apply runs under a short-lived, least-privilege credential, restricted to the approved plan. Anything outside that plan, or any credential drift, fails closed.
- Audit trail Proposal, verification output, policy decision, approver, plan hash, and apply log are written to an immutable record. An apply that cannot be fully attributed is treated as an incident, not a success.
Situation. A fintech platform team wants agent-driven throughput on its Terraform-managed AWS estate, but its security posture forbids any unattended apply. The throughput gain goes unrealized because no one will let agents near production infrastructure.
Path
- 01 Map the change types agents may propose and the policy each must satisfy: network exposure, key lifetime, tagging, blast radius.
- 02 Wire machine-verified guardrails into CI: type checks, tests, terraform plan, drift detection, and policy-as-code evaluated against the plan.
- 03 Stand up the production-safety gate so no apply runs without a passing plan, a clean policy result, and a recorded human approval bound to that plan.
- 04 Run agent-proposed changes through the governed workflow on low-risk infrastructure first, widening scope as evidence accumulates.
- 05 Hand over the runbook and enablement so the team operates the gate after the engagement.
Shape of outcome. Agent-proposed changes move at speed, but every apply is gated, attributable, and reversible: the throughput becomes usable because security and platform leads can approve it without trusting an unattended agent.
Representative: illustrates the method, not a specific client.
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