Specialist agents, each with defined scope, collaborating across all your data sources.
When one agent is not enough. Multiple agents each own a domain, collaborate on complex diagnosis, and produce fully cited output, without a human analyst in the loop.
You have complex investigations that require specialist knowledge across multiple systems.
Some processes cannot be handled by a single agent because they span multiple domains, require cross-referencing multiple data sources, or involve competing hypotheses that need independent evaluation. These are the problems most AI tooling cannot touch.
We architect multi-agent systems where each agent owns exactly one domain, one data source, one diagnostic lens, one reasoning step. Agents collaborate through structured protocols, not free-form prompts. Read-only access is enforced at the framework level, not by prompt engineering.
Specialist investigates manually across StubHub Pro, MongoDB, source code. Hours per incident. Inconsistent write-ups. No reproducible evidence trail.
Incident submitted. Agents cross-reference all data sources. Fully cited RCA posted to Azure DevOps. Analyst reviews evidence, does not gather it.
How we build it.
Every stage has a defined role and a quality gate before the next one runs, the same engineering spine under every Agentix system.
- 01
Incident & Domain Mapping
We catalogue every incident type, every data source, and every diagnostic step. This defines the agent roster and their scope boundaries.
- 02
Data Access Layer
FastMCP tools for every external system, read-only, schema-validated. The Step Engine enforces this at framework level, not prompt level.
- 03
Agent Orchestration
Orchestrator assigns tasks, specialist agents execute, evidence is aggregated. Checkpoint loop re-opens investigation if confidence does not converge.
- 04
Eval Gate
Golden YAML fixtures capture known incidents with expected citations. CI blocks any PR that drops precision or increases calibration error.
- 05
Shadow Mode
New pipeline versions run in parallel with production, same incidents, outputs not delivered. Compare → validate → flip flag. No production risk from upgrades.
- 06
Monitoring
Per-tenant isolation verified continuously. Prometheus metrics, OTel traces, Langfuse on every multi-agent run.
Delivered on
every engagement.
You own all of it, code, infrastructure and data, from day one. No licence fees, no hosted dependency.
- Multi-agent architecture with specialist agents per data source or diagnostic domain
- Read-only enforcement at the framework level, structural, not policy-based
- CI regression gate with golden fixtures, blocks PR on precision drop or quality regression
- Shadow mode deployment, new pipeline version validates against live incidents before traffic shifts
- Per-tenant data isolation verified by integration tests on every build
- Full observability, Prometheus metrics, OTel traces, Langfuse on every agent run
Not a demo. In production.
Open-source.
Self-hosted. Owned.
Built on components you can host, fork and extend. LiteLLM abstraction means you swap models without rewriting integrations.
Is this your
problem shape?
Describe what you're trying to do. We'll confirm whether this is the right system type, scope the build, and tell you what the eval harness looks like.