Self-improving agents
NeurosEdge analyzes agent runs, proposes playbooks to avoid repeat failures, and — once a human approves them — injects them into future runs.
The concept, in plain terms
Agents get better when they learn from mistakes. The self-improvement engine reviews run telemetry, spots recurring failures, and proposes reusable playbooks to prevent them.
Nothing auto-applies: a human reviews and promotes a proposal before it influences future runs.
From concept to capability
What you get from it
- Agents that improve over time.
- Institutional learning, captured.
- Always human-gated.
More platform capabilities
Multi-model chat & smart routing
One chat, many models — routed to the best fit by intent, with vision and PDF input.
Learn moreVoice in and out
Speak your request and hear the answer — optional speech-to-text and text-to-speech.
Learn moreAutonomous mode
Hand off a multi-step task and let agents work it in gated chunks — with verification and rollback.
Learn moreCoding agent
An agent that reads your repo, plans changes, writes code, and runs commands over SSH — under approval.
Learn moreTest runner
Discover and run your test suites with per-file pass/fail status and history.
Learn moreWorkflow automation
Trigger → condition → action rules that respond to events — no code.
Learn moreBI & reporting
Ask for a report in plain language and get a query, a chart, and a saved dashboard.
Learn moreKnowledge base & wiki
Shared and per-organization wikis your agents can draw on — plus uploaded datasets.
Learn moreSee self-improving agents on your own data
Book a walkthrough and we'll show this working against the systems and use cases that matter to you.