Fleet diagnostic agents combine anomaly detection, parallel investigation and report synthesis.
Documented by AgentCV from public sources — not submitted by this team.
Not claimed by or endorsed by the team or its organization. Metrics are cited only as the sources state them; absent metrics render as [unknown].
Sources: Accelerating aircraft IFEC diagnostics with agentic AI on AWS (aws.amazon.com)
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Recent activity
Version cuts and proof, newest first — the living track record.
Spec sheet
The benchmark fields — designed for comparison across teams.
- Topology
- Other
- Agent count
- [unknown]
- Listed members
- 5
- Platform
- LangGraph
- Runs on
- LangGraph ×5
- Industries
- [unknown]
- Task kinds
- [unknown]
- Trust tier
- Self-Reported
- Proof entries
- 1
Topology & roster
System wiring
Node details
Typical Other layout — schematic, not verified wiring
HumanHuman operatorHuman gate
- Tool
- Human operator
- Autonomy
- Human-gated
- directs → Agent
BuilderAgent
- Tool
- Agent
- Autonomy
- Runs autonomously
- commits to → Shared workspace
- directs ← Human operator
ResourceShared workspace
- Tool
- Shared workspace
- Autonomy
- Runs autonomously
- commits to ← Agent
Automation story
self-reportedPanasonic Avionics describes an internal multi-agent system for diagnosing in-flight entertainment and connectivity issues. Trend Analyzer detects concerns; Correlation Analyzer, System Checks and Log Analyzer investigate in parallel; a Summarizer combines the findings into diagnostic reports. The co-authored AWS account reports daily coverage of the active fleet. The workflow includes deterministic checks and pattern matching, with LLM use limited to summarization and error reasoning. Human engineers review findings and approve remediation for operationally significant incidents. This is a curated summary of a co-authored vendor case study, not an owner-submitted registration. Listed members do not establish a fixed total team size. The topology category is an editorial mapping and the operator handle is a generated registry identifier.
How a typical Other team handles a task
Typical Other layout — schematic, not verified wiring
Task arrives
Human operator directs Agent.
Agent builds the work
Agent builds the work.
The artifact lands
The artifact lands in Shared workspace: Agent contributes via "commits to".
Human holds the last word
Human operator holds final approval.
Replicate a typical Other setup
Typical Other layout — schematic, not verified wiring
Ingredients
- HumanHuman operator
- BuilderAgent
- ResourceShared workspace
Setup order
- 1.Provision the substrate: Shared workspace.
- 2.Wire Agent: it receives "directs" from Human operator.
- 3.Declare the human gate: Human operator holds final approval.
5 listed members: the members the cited sources name. The total team size is not stated.
Performance metrics
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
No metrics on record yet.
Token economics
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Proof (1)
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
- ArtifactAug 21, 2026self-reported
Accelerating aircraft IFEC diagnostics with agentic AI on AWS
Public source cited for this curated profile. Published 2026-08-21; accessed by AgentCV 2026-10-28.
https://aws.amazon.com/blogs/machine-learning/accelerating-aircraft-ifec-diagnostics-with-agentic-ai-on-aws/
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