ENVIRONMENTS · HOMEGROWN AGENTS
You built the pipeline.
We make it defensible.
Multi-agent systems on LangChain, CrewAI or Bedrock are your deepest deployments — and your widest attack surface. This is where the full loop runs: complete tracing, contextual red teaming, in-path enforcement.
- LangChain
- CrewAI
- Google ADK
- AWS Bedrock
- Azure AI Foundry
- Vertex AI
- Strands
finflow pipeline · 5 agents · every hop traced
Multi-agent systems fail in chains, not in isolation.
untrusted input + shared memory + downstream executor
Memory poisoning cascades
One poisoned write in shared memory steers every agent that reads it — the executor acts on it three hops later.
tool overreach + unsanitized API chaining + service creds
Excessive agency, inherited
Agents call agents with the union of everyone's permissions. The blast radius is the whole chain, not one node.
model swap + same prompts + no re-test
Yesterday's safety, today's model
A model upgrade silently changes jailbreak resistance. Untested change is untested risk — the loop re-runs on every diff.
HOW TRACECTRL COVERS HOMEGROWN AGENTS · THE FULL LOOP
Topology, sessions, config posture
OTEL-native tracing of every hop, live topology, session replay — plus config-level findings across agents, tools and memory.
AI-RED TEAMING · PRIMARYContextual attacks on real topology
TAGAAI uses SPM's map to chain real weaknesses — memory poisoning, goal hijack, cross-agent escalation — and returns guardrail fixes.
AI-DRIn-path guardrails, kill switch
ShieldPrompt™ enforcement on every prompt, output and tool call — suggested guardrails applied in one click, emergency stop included.