Agent Factory & Operations for High-Stakes Digital Labor
Most AI agents are guessed into existence, assembled from tools and prompts, and governed by hope.
Three principles guide every build.
How work gets done already lives in your procedures, KPIs, and operational logs. Behavior should be mined and compiled from that blueprint, not rebuilt by hand for every agent.
Building the machinery that produces agents delivers consistent quality and economies of scale on every build. The advantage is industrialization, in place of one-off builds.
Engineering discipline, controls, and evidence belong in the machinery itself, standard on every governed agent, with human gates on key steps, so trust is earned on every run.
Across five connected stations, the platform manufactures, evaluates, runs, and improves your governed agents to meet your business standards.
Learn mines operational knowledge from procedural documents such as SOPs and workflows, alongside labeled cases and operational logs, to create your Operational Knowledge Model.
Output: an Operational Knowledge Model of process, decisions, KPIs, policies, and guardrails, passed to Compile.
Procedures and operational evidence establish the intended, actual, and effective paths through the work. Learning agents produce an Operational Knowledge Model that defines work and decisions, controls and outcomes, through process, KPIs, policies, and guardrails. This blueprint feeds Compile and supplies evaluation and assessment standards.
The knowledge model enters the agent factory. The output is a versioned governed agent image with instructions, tools, memory, and guardrails. The supervisor and specialist agents illustrate one possible architecture; the operational blueprint determines the architecture for each build. The image feeds Evaluate.
Evaluation agents test the agent endpoint against the blueprint and test suite. Safety, security, privacy, and bounded agency are must-pass checks. Reliability, procedural, service, and fairness receive graded quality scores from zero to ten. Regression is checked against the last good build. Passing builds go to Operate. Failed builds send findings to Recalibrate.
Governed agents execute production work. On a separate observation path, Insight agents compare each run to the blueprint. They track reliability, groundedness, safety, quality of service, and run cost, with fleet health and changes over time on a zero-to-one-hundred scale. Root cause, recommendations, and an audit trail support findings sent to Recalibrate.
Evaluation failures and production findings enter feedback agents. They propose a change for human review. After approval, a new version returns to Evaluate before release.
Your models, data, and decisions remain under your control, with access governed by the policies you already run.
A single-tenant deployment runs in your cloud or on-prem, with governed agents inside your infrastructure and security boundary.
Your Cloud VPC / Tenant· Governed agents on KubernetesGoverned agents use your identities and permissions, retrieve secrets from your vault, and access external services through the security gateway.
Agent Security Gateway· Agent Identity & Governance· Secrets & Key VaultAction records and decision evidence stream to your SIEM, so your security team can review agent activity and investigate individual runs.
SIEM Integration· Audit streamHigh-stakes steps require human approval during operation. Changes to governed agents require approval and evaluation before a new version enters production.
Runtime approval gates· Approval & evaluation before releaseDeploys within your existing regime HIPAA SOC 2 GDPR PCI DSS ISO 27001
Governed agents carry out policy-bound work, record decision evidence, and route high-stakes steps for human approval.
Handle benefits, eligibility, coverage, and claim-status inquiries using approved information and policies, with exceptions routed to your team for review.
Support identity and sanctions checks, triage alerts, and draft suspicious-activity narratives under your policies, with findings and evidence routed for human review.
Handle first-notice intake and prepare coverage and settlement recommendations against policy terms, with evidence and high-stakes decisions routed for human approval.
Classify cases, assemble evidence, track required timelines, and draft responses under your procedures, with proposed resolutions routed to your team for review.
Assemble evidence, draft reason-code rebuttals, and track network deadlines under your dispute procedures, with proposed responses routed to your team for review.
Assess submissions against your underwriting guidelines and prepare recommendations with recorded evidence, with exceptions and high-stakes decisions routed for human approval.
Start with one process. We build its governed agent, prove it against your own outcomes, and run it inside your boundary.
Bounded Autonomy is working with a small number of design partners ahead of our public launch. If you run operations where a confident wrong answer is a liability, the founders want to hear from you.