Glen handles the investigation.
Humans handle the authority.
Operational incidents cross databases, webhooks, and third-party APIs. Today, engineers manually stitch fragments together. With Glen, the system gathers the evidence and pauses only where human decision belongs.
Work is an object, not a prompt.
Prompts disappear into context windows. Work requires durable identity, scope ceilings, evidence provenance, and signed outcomes.
In conventional AI agent setups, this string is immediately dumped into a model prompt with unconstrained tool definitions. If the session terminates, all state and reason traces are lost.
Glen connects the dots humans normally stitch together.
Instead of forcing an on-call engineer to open four dashboards, grep ingress access logs, and query Redis, Glen assembles telemetry synchronously into an adaptive investigation graph.
A model's claim isn't evidence until Glen verifies it.
LLMs generate convincing stories about why a system failed. Glen treats language models as hypothesis generators, never as truth sources. Raw facts stay separated from claims.
Autonomous doesn't mean uncontrolled.
Glen never executes mutations under an open-ended agent mandate. Authority is evaluated per action. Safe read operations proceed autonomously; production writes require a scoped human lease.
Root cause validated: Unmapped carrier enum 'AT_TERMINAL_OUTGATE'. Requesting permission to dispatch 37 replay tasks into BullMQ worker fleet with 15m rate clamp.
Execution isn't success. Reality is.
Most autonomous agents receive an HTTP 200 OK from an API tool and immediately declare victory. Glen assumes tools can succeed while reality remains broken. Glen queries production ground truth to verify the actual real-world outcome.
Glen dispatched 37 batch replay tasks to the ingestion worker cluster under the approved 15m lease.
HTTP 200 OK · 37 replay jobs scheduledGlen re-queries PostgreSQL to verify whether the actual shipment records transitioned to their correct updated state.
Everything Glen does becomes explainable later.
When an incident concludes, Glen doesn't leave an ephemeral LLM transcript. It seals an immutable audit package and a second-by-second execution scrubber that engineers and auditors can inspect years later.
One control plane. Many runtimes.
Glen separates governance from model inference. Your security policies, authority checks, and audit trails remain identical whether work runs on Google Gemini, OpenAI, or NVIDIA NIM.
Production provider for structured reasoning & extraction.
Supported execution adapter via structured schema tools.
Evaluation of high-throughput microservices for sovereign & on-prem deployment.
Roadmap execution adapter via standard tool-use schemas.
Designed for localized on-premises LLM endpoints.
model → tool calls. Glen governs the complete lifecycle: work → authority → execution → evidence → recovery.Glen works where your work already happens.
Glen doesn't ask you to migrate your operations to another siloed dashboard. It acts as an authoritative operational hub, orchestrating across your existing messaging, telemetry, queues, and databases.
Interactive human sign-off block in channels
approved: 15m batch replay lease by AlexGive Glen a problem.
Start with one critical operational workflow. Expand when you've proven the authority boundary and witnessed real-world verification in action.