AWSJust 5 min to take controlThe AI Workforce
Management System
Software agents and robots, managed like a workforce — hired, credentialed, governed, observed, and continuously improved. Compliance, security, observability, and intelligence in one control plane.
Your AI workforce has two kinds of worker. Both need managing.
Software agents and embodied robots do real work, take real actions, and carry real risk. Tuning Engines manages them the way you manage people — with identity, permissions, supervision, performance review, and an audit trail.
- Identity and credentials issued per agent
- Least-privilege tool and data permissions
- Policy checks inline on every call
- Full trace of actions, cost, and outcomes
- Task authorization before execution
- Safety envelopes and operating boundaries
- Telemetry streamed into the same control plane
- Incident evidence captured automatically
Same management system · Same policies · Same audit trail
Enterprises are hiring AI workers faster than they can manage them. Nobody owns the org chart.
Agents access data, call tools and trigger workflows; robots move, inspect and act in the physical world. Both create financial, safety, security and compliance exposure in real time. Dashboards after the fact are not management — the workforce needs credentials, supervision and enforcement while the work is happening.
Four pillars of AI workforce management
GRC plus the operational controls that governance alone cannot deliver — enforced inline, across software agents and robots.
- · Policy-as-code with versioned approvals
- · Control mapping to your frameworks
- · Evidence captured per worker and per task
- · Reviewer workflows for sensitive actions
- · Identity for every agent and robot
- · Scoped tool, data, and action permissions
- · Guardrails, redaction, and prompt defense
- · Inline enforcement before execution
- · End-to-end traces across multi-step work
- · Robot and agent telemetry in one timeline
- · Incidents, failures, and escalations
- · Spend attribution by team, task, and worker
- · Capture real work into datasets
- · Evaluations tied to business outcomes
- · Fine-tuning and model specialization
- · Routing to the best-fit, lowest-cost engine
The AI workforce lifecycle
One repeatable operating model, whether the worker is a software agent or a machine on a factory floor.
Register every agent and robot as a managed worker with an owner.
Issue identity, keys, scopes, and the tasks each worker may perform.
Route work through the control plane — SDK, gateway, or observe-only.
Enforce policy inline, approve sensitive actions, cap spend in real time.
Score outcomes, quality, safety, and cost against business targets.
Turn real work into datasets, tuned models, and better routing.
Two ways to deploy. One control plane.
Start with sidecar for visibility with zero traffic changes — the mode we run today for robots and embodied workers. Turn on proxy mode for software agents when you want enforcement inline.
Nothing sits in the request path. We capture traces and telemetry from your agents and robots, so you get full visibility from day one.
- Full observability — traces, tool calls, latency, spend
- Intelligence management — datasets, evals, tuning candidates
- Async compliance and risk review on captured activity
- The only mode for robots / embodied workers today
Point your SDK at Tuning Engines and every call is governed before it executes — plus everything sidecar gives you.
- Runtime security and guardrails inline
- Policy-as-code checks and approvals
- Model routing, failover, and caching
- Budget caps enforced per request
- Runtime security & compliance for robots — coming soon
Same policies · Same audit trail · Sidecar first, proxy when you are ready
Your AI workforce is now an operating expense.
Software agents are running workflows. Robots are taking physical actions. Both consume budget, touch regulated data, and act on your behalf. Tuning Engines manages that workforce the way you manage people — compliance, security, observability, and intelligence in one place.
Prove every worker followed the rules.
Policy-as-code evaluated on every action — inline in proxy mode, asynchronously on captured traces in sidecar mode.
- Run the inference testing matrix across models and workers before anything reaches production
- Execute external compliance tests and risk management workflows with tracked remediation
- Redact PII with high-signal data filters before it reaches models, tools, or robots
- Simulate policy decisions in shadow mode — test before you enforce
- Require approvals for sensitive or high-risk worker actions, with full evidence trail
- Export audit-ready evidence packs for EU AI Act, NIST AI RMF, and SOC 2 reviews
Token usage is not business value.
Tuning Engines maps every work session — agent or robot — to the outcome it produced, so contributors, team leads, and executives each see the view that matters to them. Spend rolls up from worker to team to org, and ROI is measured against real business results, not token counts.
Contract review workflow — $0.0031/outcome · 2,340 sessions mapped to 18 closed deals this quarter.
Different roles.
One workforce management system.
Your AI workforce is growing. Prove it's working.
- AI cost baseline across all teams
- Savings assessments and recommendations
- ROI measurement tied to business outcomes
- Budget enforcement and spend alerts
Standardize how every AI worker is run.
- Single control plane for all AI workloads
- Consistent policies across models and vendors
- Full audit trail for compliance and governance
- Infrastructure strategy with transparent comparisons
Stop the breach before the worker makes the call.
- Granular agent and tool access controls
- Runtime guardrails and approval workflows
- Audit logs and compliance evidence exports
- Secure credential and key management
Run agents and robots on one operating model.
- Unified control plane for agents, tools, and robot fleets
- Task authorization, safety envelopes, and telemetry
- BYO endpoints and fine-tuned model support
- Governance built in — not bolted on
FAQ
What exactly is an "AI workforce"?
Every non-human worker doing work on your behalf: software agents (copilots, autonomous agents, MCP tools, workflow agents) and embodied robots (warehouse, inspection, service, field). Tuning Engines manages both under one identity, policy, and evidence model.
How do robots fit into the same system as software agents?
Robots are registered as managed workers with an owner, credentials, and an authorized task list. Task requests are checked against policy and safety envelopes before execution, telemetry streams into the same timeline as agent traces, and incidents produce the same audit evidence.
How quickly can we onboard our first workers?
Most teams register their first agents and have a governed baseline within a business day. The integration is lightweight — route existing API calls through Tuning Engines, or start in observe-only mode with no traffic changes.
Does routing work to lower-cost engines reduce quality?
Not necessarily. Many tasks are over-served by frontier models. We show quality, latency, and cost comparisons drawn from your actual work, and you stay in control of what moves and what stays.
What are tracking modes, and which should we start with?
Proxy mode routes traffic through Tuning Engines so policy, budgets, routing, and runtime security are enforced inline. Sidecar mode captures traces out of band via SDK, OpenTelemetry, or gateway analytics for full observability, intelligence management, and asynchronous compliance and security. Most teams start in sidecar, see what would have been blocked, then flip to proxy.
Which tools and runtimes does it work with?
Claude Code, Codex, Goose, OpenCode, Aider, Cline, and Roo; CLI and npx; SDK tracing; MCP for ChatGPT, Claude, and IDEs; the inference API and model registries; enterprise provider setup on AWS, Azure, GCP, and Cloudflare; and orchestration through LangGraph and Temporal.
How does governance work without slowing down AI teams?
Policies, budgets, and permissions are configured once and enforced automatically. Teams work through the same OpenAI-compatible endpoint they already use — governance is applied in the control plane, not in application code.
What compliance frameworks does Tuning Engines support?
Tuning Engines generates evidence coverage for EU AI Act Readiness, NIST AI RMF (Govern, Map, Measure, Manage), and SOC 2 AI Controls. The audit log, policy enforcement records, and approval trails are structured to produce ready-to-export evidence packs for each framework — typically with most controls marked ready from day one of connection.
Run your AI workforce like a business unit.
Track outcomes per agent. Enforce policy inline. Cut inference spend 20%+.