The lab drifts.
Inventory, readiness, power, console access and telemetry need to agree before a run is safe.
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LabAI
AI agents that can safely operate a real lab.
LabAI is a reference architecture for secure agentic AI: a system where people can ask for complex work in plain language, while authority stays behind bounded personas, OpenBao-protected secrets, typed tools, deterministic workers and reviewable evidence.
Lab operations
Secure agents
Model economics
Evidence by design
What problem does LabAI solve?
A hardware lab is where generic AI agents hit production reality.
The request sounds simple: prepare machines, run a hardware test, recover failures and explain the result. The reality is a moving lab estate with changing host state, powerful out-of-band controls, specialist test scripts, credentials that must stay hidden and long-running work that cannot depend on a chat window.
LabAI turns that messy operating environment into an agentic system with clear actors, bounded authority, recoverable execution and evidence from every run.
Intent needs structure.
A tester's goal has to become hosts, timing, parameters, retry rules, evidence collection and report instructions.
Authority must be bounded.
The agent needs tools, but secrets, write access and recovery actions have to stay behind policy gates.
What does the solution architecture look like?
A layered control plane, not a chatbot with root access.
LabAI separates conversation from authority. The user-facing edge helps people express intent; the LabAI master owns capability, policy, secrets, inventory, plugins and execution. The model only sees the tools its active persona is allowed to use.
Actor layer: lab technician, hardware tester and test author are treated as different jobs with different authority.
Persona layer: each actor gets a LabAI persona shaped around the work they actually perform.
Capability layer: MCP tools are split into read and write tiers, exposed through allowlists and brokered by the LabAI master.
State and secret layer: inventory is canonical, while OpenBao holds operational credentials behind scoped service tokens.
Execution layer: reviewed briefs go to a worker that claims lab hosts, runs deterministic plugin steps and records evidence.
Why has it been done this way?
Because agentic AI risk is mostly architecture risk.
LabAI avoids the common trap of making the model responsible for everything. It lets the model do the things it is good at: interpreting intent, asking clarifying questions, shaping arguments and judging unfamiliar exceptions. Everything repeatable, sensitive or long-running is owned by deterministic services.
Least privilege first
Secrets outside prompts
Smallest capable model
Bounded personas
A prompt-injected persona can only see the tools its actor role is allowed to use.
OpenBao-backed services
Credentials stay with scoped services instead of chat, prompts, plugins or run briefs.
Model-tier aware
Small models handle fast paths; expensive reasoning is reserved for ambiguity and review.
Worker-owned execution
The worker owns host claims, plugin lifecycle, retries, artifacts and terminal state.
How the run works
Chat creates the contract. Code does the work.
This is the practical difference between a demo and an operating system. The hardware tester writes a brief with the how, where, when, duration, recovery policy, collection targets and reporting needs. Execution then becomes a controlled pipeline with host claims, typed plugin steps, bounded recovery and evidence.
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01
Ask
Human intent enters through the persona that matches the actor.
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02
Brief
The output names scope, timing, retry limits, data handling and report instructions.
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03
Claim
The worker reserves managed lab hosts to prevent collisions.
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04
Run
Typed plugin steps deploy, start, monitor, collect and clean up.
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05
Recover
Known drift takes deterministic fixes; unknown drift is bounded.
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06
Evidence
Artifacts, terminal state, reports and action logs remain inspectable.
What can Ignitize solve from this example?
The same architecture discipline applies anywhere AI needs to act.
LabAI is about lab operations, but the capability it demonstrates is broader: turning AI from a risky interface into a secure operating layer for complex technology environments.
The real value is not "add a chatbot." It is decomposing work into actors, designing useful AI personas, securing tool access, choosing the right model path and making outcomes governable.
Turn users and workflows into useful AI personas.
Identify the actors, decompose the work, define what judgement belongs to people, and give each persona only the tools it needs.
Connect AI to real systems without handing it the keys.
Design capability boundaries, vault-backed credentials, policy gates, audit trails and safe escalation paths.
Use the right model path for cost, quality and speed.
Separate deterministic work from reasoning work, evaluate smaller models for fast paths, and reserve expensive models for real ambiguity.
Make AI outputs reviewable, repeatable and supportable.
Turn conversations into briefs, decisions into logs, exceptions into escalation paths and outcomes into evidence.
Build AI systems that can survive production reality.
Handle queues, retries, state, failures, permissions, secrets, observability and lifecycle management as first-class architecture concerns.
Move beyond proof-of-concept AI.
Replace fragile demos with systems that security, operations and engineering teams can understand, govern and improve.
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Need AI agents that are secure enough to operate real systems?
Ignitize designs AI-enabled architectures where the hard parts are handled deliberately: actors, authority, data, secrets, model economics, execution and evidence.