AI Agents
Six purpose-built agents for IC design and infrastructure teams — each one understands your environment, not just your prompts.
- Forge AI — cloud infrastructure from plain language, human-approved
- Avestra — spec-to-SVA pipeline, <1% hallucination rate, sign-off-ready assertions
- Analytics AI — deep insights across compute, licenses, and operations
- Ask AI — conversational access to your full operational knowledge base
- PV Agent — physical verification triage, traced to the source
- DV Agent — design verification, test generation, and coverage
From a sentence to running, verified infrastructure
Forge AI turns plain-language requests into deployed, verified AWS infrastructure — with a human approval step before anything is provisioned. It generates CloudFormation or Terraform, validates it, presents a plan, and pushes the final IaC back to Git after deployment.
Human-in-the-loop approval
Every plan — resources, warnings, and generated IaC — is presented for review before anything is provisioned. Choose Approve, Discuss, or Reject. Nothing runs without your sign-off.
- Automated infrastructure provisioning from natural language
- Drift detection and autonomous remediation
- IaC generation and review (Terraform, CloudFormation)
- Multi-cloud resource orchestration (AWS, GCP, Azure)
- Policy enforcement and compliance guardrails
- Auditable change history and rollback support
From spec and RTL to sign-off-ready assertions
Avestra transforms specifications and RTL designs into production-grade SystemVerilog assertions using an 8-agent orchestrated pipeline. Built on 35 years of CPU/ASIC/SoC domain expertise, it achieves a <1% hallucination rate — delivering sign-off-quality SVA suites in minutes, not days.
<1% hallucination rate
Proprietary RAG trained on Ashok Mehta's 3 SVA textbooks, 22 US patents, and 35 years of CPU/ASIC/SoC expertise — domain intelligence no generic LLM can match. Deterministic, auditable output every run.
- 8-agent orchestrated pipeline: spec analysis, RTL analysis, assertion generation, testbench gen, cross-check, and more
- Spec ↔ RTL inconsistency detection — catches missing states, illegal transitions, and timing violations before first sim
- Production-grade SVA suite — clocked assert, assume, cover, vacuity-guarded, multi-clocked, every assertion traced to source
- Coverage-driven SV testbenches with UVM-ready architecture, directed tests, and response checkers
- LLM-agnostic pipeline — swap the underlying model without affecting output quality or structure
- Works with Questa (Siemens), VCS (Synopsys), and Xcelium (Cadence) — no simulator vendor lock-in
Deep operational insights across your entire environment
Analytics AI surfaces actionable insights from your infrastructure, EDA tools, and operational data. It aggregates usage across cloud, on-prem, and licenses — giving engineering and finance teams a unified view with anomaly detection and natural language querying.
Natural language over your operational data
Ask questions like “which team spent the most on EDA compute last quarter?” or “flag licenses approaching renewal.” Analytics AI queries your data and returns structured insights — no SQL, no dashboards.
- Infrastructure usage attribution across teams, projects, and cost centers
- Anomaly detection across compute, license, and operational metrics
- Trend analysis and utilization insights across your full environment
- Custom dashboards and reports for engineering and finance teams
- Cross-platform data aggregation — cloud, EDA, and on-prem
- Natural language queries over your operational data
Why did our GPU cost spike on March 14th?
Querying Stratos cost data and deploy history…
Spike correlates with the model-training-v3 batch job that ran 14:00–22:00 UTC. 3× normal GPU utilization. Triggered by the ml-pipeline team. Estimated excess cost: $1,240. Recommend adding a budget alert for this job class.
Has this happened in the last 7 days?
Yes — 2 incidents in 7 days. Both resolved within SLA. Root cause: Redis connection exhaustion during peak traffic (8–9pm UTC). Alert threshold already adjusted after incident #1.
A chatbot that actually knows your environment
Ask AI is the conversational interface to your entire operational knowledge base. It retrieves answers from Stratos runbooks, incident history, monitoring data, and documentation — giving your team instant context without digging through dashboards or tickets.
MCP connector support
Ask AI connects to any data source via MCP — your ticketing system, monitoring tools, databases, and internal wikis. One interface for your entire operational knowledge base, with self-hosted or cloud LLM inference.
- RAG across Stratos docs, runbooks, and tickets
- Database and analytics query access
- MCP connector support for any data source
- Multi-turn conversation with full context retention
- Team-scoped knowledge with access controls
- Works with self-hosted and cloud-hosted LLMs
Physical verification triage — traced to the source
PV Agent triages DRC, LVS, and ERC failures — identifies root causes, traces violations back to the responsible commit, and surfaces fix suggestions with full runbook context. Sign-off moves faster when every error already has an answer.
IP stays on-prem
PV Agent supports self-hosted LLM inference — your design data never leaves your environment. Required for tape-out sensitive and export-controlled designs.
- DRC, LVS, and ERC triage with root-cause identification
- Fix suggestions with cross-project runbook context
- Commit-aware: traces violations back to the responsible change
- Batch violation analysis across full chip or block level
- Self-hosted or cloud LLM inference — IP stays on-prem
- VS Code extension for inline access during sign-off
Test generation and simulation — without the manual effort
DV Agent understands your testbench, coverage reports, and design specs. It generates tests for uncovered paths, runs simulations, and automates regression management — so your team closes coverage faster without manual effort.
VS Code Extension
DV Agent is available directly inside VS Code. Inline suggestions, diff-aware context, and chat — without leaving your editor. IP stays on-prem with self-hosted inference.
- Test generation for uncovered paths, FSM states, and corner cases
- Simulation run management and regression automation
- Coverage analysis and gap identification across functional and code coverage
- Directed test authoring from coverage holes — no manual testbench edits
- Spec-aware context from your runbooks and design docs
- Formal verification setup and result interpretation
Put AI to work in your environment
See how all six agents work together inside your existing infrastructure and EDA workflows.