50+ Pre-Built Agents. Every Domain Covered.

Hybrid. Each session opens with a materialized snapshot of the relevant vault tiers (L1 → L4 merge, project overrides personal overrides company overrides platform). Hot context - agent definition, skill bindings, active brief - is injected at session start. Cold context - full vault corpus - is retrieved on demand via dense + sparse retrieval with reranking. Typical session-start overhead: 2,000–8,000 tokens depending on tier merge depth. P50 retrieval latency: under 600ms. Telemetry on every materialization is captured in the vault session log (389 sessions to date).

Provider routing is config-driven. Default ships routes to Anthropic and OpenAI; client may swap to Azure OpenAI, AWS Bedrock, Google Vertex, or self-hosted (vLLM, Ollama) via the same router config - no code changes. For data residency: vault contents stay in client tenancy; outbound LLM calls carry only the materialized session context, and the redactor strips configured PII / NDA-flagged tokens before send. On-prem option available for enterprise tier - full deployment behind client VPC, no outbound calls except where explicitly whitelisted.

Two layers. (1) Pre-deployment simulation harness: every agent definition runs against a curated suite of golden-path and adversarial scenarios. Failure to ship the expected gate verdict on a known-bad input blocks promotion to production. (2) Production drift detection: every gate decision is sampled into a regression suite. Behavioral changes that drop pass-rate below the configured threshold trigger an alert + rollback option. Regression-suite coverage and pass-rates available under NDA - the methodology is more important than the headline number, and the methodology is reviewable.

Vault-encoded behavioral constraints, not prompt-injection or fine-tuning. Constitution rules live as YAML files under vault/rules/, materialized into agent context at session start. They take the form of declarative gates ("never publish without HITL approval on material exposure", "never call external endpoints when context contains NDA-flagged tokens", etc). Violations are caught at the gate layer - the agent does not ship the violating action, by construction. This is closer to AWS IAM than to constitutional-AI training: declarative, version-controlled, reviewable, and enforced at the action surface, not embedded in model weights.

Three named gates ship in the default platform: (1) Triage Accept - agent triages incoming work and proposes routing; human (or higher-tier agent) accepts or redirects. (2) Plan Approval - agent produces a structured plan with anticipated cost, side-effects, and rollback path; human approves before execution. (3) Verification Accept - agent reports completion with outputs and self-graded confidence; human verifies before merge / send / ship. All three gates persist their decisions to vault session log. Custom gates can be added per-agent via the constitution layer (e.g. "Compliance Approval" for material-exposure decisions).

Auditability + rollback + diff-review. Every agent is a YAML file in version control: name, role, vault bindings, skill access, gate requirements, model tier, cost cap. Changes flow through the same review process as application code. If an agent starts producing degraded output, you git blame the change, git revert it, and you're back to the prior known-good behavior. No "the model just changed today, we don't know why" - the change is in the diff. Same playbook as managing infrastructure-as-code.

Those are frameworks for building agentic systems. PlexiFlexor is the opinionated platform with the agentic system already built - 50+ specialist agents in the production catalog, 30+ composable skills, the 4-tier vault hierarchy, the HITL gate taxonomy, and the constitution layer. A team building from a framework starts at line zero of every artifact (agent definition, skill registry, vault loader, gate engine, eval harness). A team buying PlexiFlexor starts with the catalog already deployed - and the focus moves to which agents fit which workflows, not how to assemble the pieces.

It doesn't prove the platform fits your workflow. It proves the platform is real, operated daily, and produces verifiable artifacts. April 27, 2026: an internal integrity-auditor advisor flagged 24 issues across this site. April 29: 12 had shipped to UAT under HITL approval. Diffs are in the public repo. Every agent that authored a commit carries attribution metadata. Every gate decision is in the session log. No competitor in the agentic-AI space ships a self-referential proof at this granularity. Full case study →