AgentOps workbench

AegisLoop — glass-box mission control

Run a bounded mission and watch specialist agents execute under policy, budget, and evaluation gates. Architecture and live SLOs on the left · honest per-agent span replay in the center · product on the right.

Mission pipeline · agent span replay

awaiting run

Principal AI Architect Operating Model

Production Incident Triage

Orchestrate specialist agents through bounded planning, tool execution, evaluation, memory updates, and ship/no-ship control.

Ready 0% quality score

Pick a mission → Run → watch real agent.execute spans (duration_ms from the API).

No spans yet — run a mission to replay the fleet.

agents — runtime n/a decision awaiting cost $0.00

Current agent Orchestrator

Current task Waiting for mission input

Scratchpad

Agent thoughts, tool outputs, and handoff payloads stream here during the run.

Orchestrator

Waiting for mission Goal owner, planner, budget governor

Specialist Agents

1. Pick a mission profile → 2. Run mission → 3. Inspect the brief and per-agent spans in the center.

Scenario

Mission profile

Bounds

Token budget 18k

Risk tolerance 42%

Policy filters Long-term memory RAG context

LLM Stack

Runtime mode

Runs fully in this browser with deterministic local reasoning. No paid API key required.

Backend API key (only if required)

Mission UI

Configure the mission, then run the agent fleet. The brief is generated by chained local agents and updated as each agent completes.

Telemetry

Loop0

Tool calls0

Cost$0.00

Latency0.0s

Fleet P50—

Fleet P95—

Failure rate—

Evaluation gate, memory, guardrails & stage map

Evaluation Gate

Awaiting evidence

AegisLoop — glass-box mission control

Mission pipeline · agent span replay

Production Incident Triage

Orchestrator

Specialist Agents

Evaluation Gate

Memory & Context

Guardrails

Trace

Goal

Plan

Execute

Verify

Ship