deep-agentic-core-mcp

deep-agentic-core-mcp is the shared MCP server layer for the DeepAgentLabs ecosystem. It is designed to expose a single MCP interface that combines:

• agenticlens style workflow inspection, profiling, and analysis
• agentic-chaos style resilience testing and fault-injection workflows
• agentic-sidecar style supervision-readiness and module-surface discovery
• authenticated local Control Tower 0.7.0 investigation and read-only fleet posture access

It sits above the AI Operations Workflow Specification, exposing a unified MCP-native control surface over the shared operational model used by the reference implementations.

The goal is one MCP server, one package, and one registry identity rather than separate MCP servers for each product surface.

Connect and use

Start with the user and developer guide for hosted signup, MCP client connection, workflow analysis, evaluation reports, and local chaos/ Sidecar capabilities. Run the Python workflow example to send an exported workflow to the hosted service. The guide documents current integration availability and distinguishes implemented tools from future runtime work.

Idea

This project is the control plane between LLM hosts and the existing Python libraries:

• agenticlens remains the core profiling and analysis engine
• agentic-chaos remains the core chaos and resilience engine
• agentic-sidecar remains the core decision-supervision and governance engine
• agenticops-control-tower 0.7.0 provides the operator-facing control plane
• the AI Operations Workflow Specification remains the shared data contract
• deep-agentic-core-mcp becomes the MCP-native interface that hosts can call

That means MCP clients can connect once and access observability, chaos, sidecar discovery, and later Control Tower-aligned operations surfaces through one server.

What This Server Should Eventually Do

Planned capability areas:

• profile an agentic workflow and return structured telemetry summaries
• analyze workflow artifacts and surface optimization recommendations
• run controlled chaos experiments against target workflows
• expose installed Sidecar decision/runtime capabilities and module inventory; package readiness is distinct from a live task connection
• eventually expose Control Tower inventory and operator-facing control surfaces once that sibling package ships them
• compare normal versus chaos runs
• expose shared resources such as workflow schemas, run metadata, and saved reports

Design Principles

• One MCP identity: publish a single server to the MCP Registry
• Python-first: package and publish through PyPI
• Thin orchestration layer: reuse agenticlens, agentic-chaos, and agentic-sidecar instead of re-implementing their logic
• Local-first: work well as a stdio MCP server for developer workflows — this matters because chaos.run_experiment executes real code (see SECURITY.md), so this server is meant for trusted, local/stdio use; authenticated HTTP deployments disable code execution
• Dual transport, one tool surface: the optional Streamable HTTP transport (below) reuses stdio tools/resources/prompts, with remote script execution excluded

Agentic Chaos v0.5 compatibility (MCP 0.3.2)

Install agentic-chaos>=0.5.0 alongside the server to discover streaming, authentication, and context-length faults through chaos.list_faults. The existing local stdio chaos.run_experiment tool runs examples/chaos_stream_target.py with faults=["stream_cut"] and returns the native degraded event. Real sibling integration tests cover the catalog and experiment path. Chaos remains optional; no hard sibling dependency is introduced. Hosted HTTP rejects chaos execution.

The example is a repository file, not a wheel-installed asset. Clone the v0.3.2 source into a mcp-server directory and set DEEP_AGENTIC_CORE_MCP_WORKSPACE_ROOT to its parent directory before starting the server. The user guide provides exact setup commands. CI checks both the pinned Chaos v0.5.0 tag and the latest sibling checkout.

See the user guide and the Chaos v0.5 API guide.

Phase 3 completion (MCP 0.3.3)

• Finding provenance (3a): every lens.analyze_workflow recommendation has a provenance block. It names the artifact (workflow_id, canonical sha256 digest), the workflow step (id, name, type, index), the evidence span IDs and the producer. resolved is true only when all of these exist in the artifact you sent, and provenance_summary counts how many do. Needs agenticlens>=0.6.1 for model-swap and chaos-impact findings.
• Chaos results as AIOS runs (3b): chaos.run_experiment attaches aios_run, an AI Operations v0.4-draft Run built by agentic-chaos>=0.5.1's own exporter and checked by the spec adapter (aios_export.aligned). chaos.export_aios_run converts a saved or session report without running any code.
• Script policy (3b): DEEP_AGENTIC_CORE_MCP_ALLOWED_SCRIPT_GLOBS and DEEP_AGENTIC_CORE_MCP_DENIED_SCRIPT_GLOBS narrow which workspace scripts chaos.run_experiment may run. Deny wins, and .venv, site-packages and .git are always denied.
• Version routing and conformance (3c): spec.validate_artifact validates against the schema set the artifact's spec_version names and rejects markers it has no schema for, citing the applicable v0.5 rule (AIOS-0.5-V01…V06). Its conformance section lists every AIOS-0.4-* rule with a status, separate from server presentation. Needs ai-operations-spec>=0.5.1 for the rule and compatibility catalogs.

Run examples/phase3_walkthrough.py from a workspace checkout to see all four end to end.

Since 0.3.4 the hosted service at https://mcp.deepagentlabs.io/mcp ships the same sibling versions and spec catalogs, so provenance, conformance reports and chaos.export_aios_run behave the same hosted as locally. Only chaos.run_experiment stays local-only.

AgenticLens 0.7.0 diagnosis tools (MCP 0.3.5)

• lens.diagnose_run — send a trace Run (and optional baseline runs) and get AgenticLens's diagnosis report: failure-taxonomy diagnoses with confidence and supporting spans, the faulty step and agent with ranked candidates, the incident timeline, robust anomalies against the baseline, guardrail status (budget caps spans, depth, and time), and an investigation narrative in Markdown (include_narrative, default true).
• lens.score_analysis — send an evaluation report and get deterministic and model-based scores reported separately, judge cost kept apart from agent cost, and the cases where evaluators disagree.

Both need agenticlens>=0.7.0; with an older install they return a clear "unavailable" error and every other Lens tool keeps working. The hosted images pin agenticlens==0.10.0 and agentic-chaos==0.8.0 (since MCP 0.3.13).

AgenticLens 0.8.0 suite tools (MCP 0.3.9)

• lens.suite_from_traces — send trace Runs plus a suite name and version and get a draft agentic-evals suite: each successful run becomes a case expecting its tool trajectory and latency/cost within a margin, marked needs_review, with PII masked (mask, strategy). Failed runs come back in skipped with a reason.
• lens.suite_check — send a suite and get its summary (splits, domains, tags, sources, review status), exact/near/conflicting duplicates, and the approval checks AgenticLens would run (review, unmasked PII by type and location only, conflicting duplicates), plus approvable.

Both are read-only and store nothing; approving into the immutable suite store stays a local agenticlens suite approve step. Both need agenticlens>=0.8.0; older installs get an "unavailable" error while other Lens tools keep working.

AgenticLens 0.9.0 ModelFit tool (MCP 0.3.11)

• lens.modelfit_recommend — send one or more experiment reports (agenticlens experiment run --save), an optional ModelFit config (categories, constraints, weights, candidate catalog, reference variant) and optional variant_metadata (provider, deployment, capabilities per variant), and get a recommendation per task category: status, model, confidence, fallback, per-case savings against the reference, and high-severity risks, plus the full ModelFit report and task-level model matrix.

Constraints fail closed, so a category with no candidate that provably meets its limits comes back as no_feasible_model in unfit_category_ids. Each source's sha256 is the digest of the report's canonical JSON. The tool calls no model and stores nothing. It needs agenticlens>=0.9.0; older installs get an "unavailable" error while other Lens tools keep working.

Unified workflows (MCP 0.4.0)

0.4.0 completes Phase 4 (Unified Workflows): core.compare_runs, core.release_check, core.incident_summary and core.export_report, plus the resource://workflows/release-check resource and a core.release_check prompt. MCP judges nothing itself: comparisons come from AgenticLens, evaluation reports go through the Agentic Evals release gate (every GateConfig threshold, strict by default, unknown keys rejected), and chaos evidence must be a chaos.run_experiment report.

The release check fails closed. Each gate is passed, failed, invalid or missing, and ready is true only when all of them pass; by default the comparison, evaluation and chaos evidence are all required (required_evidence narrows that). Hosted HTTP supports all four tools; pass a locally produced chaos_report because hosted chaos execution stays disabled. The exported report is MCP-local, not an AIOS artifact. No sibling release or dependency floor changes. See the unified workflows guide and examples/unified_release_check.py.

MCP Surface (current, 0.4.1)

• core.health — rich diagnostics: adapter availability/version, loaded tool/resource/prompt counts, workspace root, recent successful calls
• core.version — server package version
• core.verify — checks agenticlens/agentic-chaos/agentic-sidecar/ai-operations-spec connectivity and reports readiness
• core.session_state — inspect what the active session has accumulated
• core.compare_runs — Lens baseline/candidate comparison stored for the release check (same behavior as lens.compare_runs)
• core.release_check — fail-closed readiness from a Lens comparison, an Evals report judged by the Evals gate, and a chaos report; missing, malformed or failed-call evidence blocks the release
• core.incident_summary — regressions, chaos crashes/timeouts, failed cases and blocked gates; no_signals when nothing failed
• core.export_report — the session's evidence as one MCP-local mcp.unified-report.v1 envelope (not an AIOS artifact)
• lens.analyze_workflow — run AgenticLens recommendations against a workflow artifact
• lens.report_summary — render a Markdown workflow report
• lens.compare_runs — compare baseline/candidate trace runs for regressions
• lens.slo_summary — apply release-gate style SLO thresholds to an evaluation report
• evals.validate_artifact — offline validation of native Evals reports and gate decisions
• lens.audit_report — case-by-case evaluation detail, optionally with HTML
• lens.diagnose_run — diagnose a trace run: faulty step and agent, timeline, anomalies, investigation narrative (agenticlens>=0.7.0)
• lens.score_analysis — deterministic vs model-based scores, judge cost, and evaluator disagreement for an evaluation report (agenticlens>=0.7.0)
• lens.suite_from_traces — draft regression-suite cases from successful trace runs, PII masked (agenticlens>=0.8.0)
• lens.suite_check — suite summary, duplicates, unmasked PII and approval readiness (agenticlens>=0.8.0)
• lens.modelfit_recommend — per-task-category model recommendations from experiment reports under hard constraints (agenticlens>=0.9.0)
• lens.routing_decide — explain runtime model selection and fallback chains from ModelFit evidence without invoking models (agenticlens>=0.10.0)
• chaos.list_faults — list the supported fault types
• chaos.run_experiment — run a workspace-sandboxed, policy-checked target script under selected faults and attach an AIOS run export (executes real code — see SECURITY.md)
• chaos.export_aios_run — convert a chaos report into a validated AI Operations v0.4-draft run artifact (added in 0.3.3; no code execution)
• sidecar.status — report installed Sidecar runtime APIs separately from live task connectivity (added in 0.3.0)
• sidecar.module_inventory — inspect the installed sidecar modules, framework adapters, and integration placeholders (added in 0.3.0)
• spec.validate_artifact — validate a workflow/run artifact against the schema set its spec_version names, with a rule-cited conformance report

Sequential tool calls can share context via an optional session_id argument, backed by an in-memory session store — see ROADMAP.md Phase 2.

See ROADMAP.md for what's shipped per phase and what's still open, and docs/tools.md for full input schemas and per-tool metadata (generated from tools/registry.py, run make docs to refresh it after changing that file).

Local vs. remote install modes

Container definitions are grouped under deploy/docker.

• Local: pip install deep-agentic-core-mcp, then run deep-agentic-core-mcp over stdio. Local session behavior is unchanged.
• Self-hosted multi-user HTTP (0.3.0+): install with pip install 'deep-agentic-core-mcp[http]', then run deep-agentic-core-mcp-http. Provision a unique bearer key per user and a Redis URL before startup. Missing configuration prevents startup.
• Hosted AWS signup: open mcp.deepagentlabs.io to generate a user identity and MCP key; connect your MCP client to https://mcp.deepagentlabs.io/mcp with its bearer key. DynamoDB stores user records and key hashes; keys work immediately and can be replaced or revoked through the page. No password or email verification is used. Save the key: it cannot be recovered. See the AWS guide.

The HTTP service uses stateless MCP transport at /mcp and Redis-backed workflow sessions scoped to the authenticated user. Users can reuse the same session_id without accessing each other's artifacts. HTTP calls cannot run chaos.run_experiment, even if the local remote-chaos override is set.

See the remote hosting guide and render.yaml for Render deployment, configuration, container builds, AWS deployment requirements, client headers, limits, and credential rotation. create_app() also accepts explicit credentials, a session backend, and allowed hosts/origins for integrations and tests. MemorySessionStore is an explicit development option; the CLI requires Redis.

Repository Layout

mcp-server/
├── README.md
├── ROADMAP.md
├── pyproject.toml
├── server.json
├── .gitignore
├── docs/
│   ├── architecture.md
│   └── tools.md          # generated - see scripts/generate_tools_doc.py
├── examples/
│   ├── sample_workflow.json
│   ├── provenance_workflow.json
│   ├── phase3_walkthrough.py
│   └── chaos_target.py
├── scripts/
│   └── generate_tools_doc.py
├── src/
│   └── deep_agentic_core_mcp/
│       ├── __init__.py
│       ├── server.py
│       ├── transport_http.py
│       ├── config.py
│       ├── prompts/
│       │   ├── __init__.py
│       │   └── registry.py
│       ├── resources/
│       │   ├── __init__.py
│       │   └── catalog.py
│       ├── schemas/
│       │   ├── __init__.py
│       │   └── tooling.py
│       ├── services/
│       │   ├── __init__.py
│       │   ├── registry.py
│       │   └── session.py
│       ├── adapters/
│       │   ├── __init__.py
│       │   ├── agentic_chaos.py
│       │   ├── agenticlens.py
│       │   ├── agentic_sidecar.py
│       │   └── ai_operations_spec.py
│       └── tools/
│           ├── __init__.py
│           ├── registry.py
│           ├── chaos.py
│           ├── core.py
│           ├── lens.py
│           ├── sidecar.py
│           └── spec.py
└── tests/
    ├── test_chaos_aios_export.py
    ├── test_chaos_script_policy.py
    ├── test_degraded_boot.py
    ├── test_imports.py
    ├── test_lens_provenance.py
    ├── test_spec_conformance.py
    ├── test_registry.py
    ├── test_server.py
    └── test_session.py

MCP-Oriented Structure

This repository should have all of the standard layers we expect for a useful MCP server:

• tools/ for callable MCP tools and their registration metadata
• resources/ for readable assets such as fault catalogs, templates, and workflow examples
• prompts/ for reusable prompt templates exposed through the server
• schemas/ for typed request and response contracts
• services/ for shared orchestration logic that keeps tool modules thin, including the in-memory session store (services/session.py)
• adapters/ for integration boundaries to agenticlens, agentic-chaos, agentic-sidecar, and ai-operations-spec — each degrades to "available": false rather than crashing server boot if its sibling repo is missing

Packaging and Publishing Model

One repository supports both the local Python package and the hosted HTTP service. The stdio entry point remains available without the optional HTTP dependencies. HTTP transport and signup support are included from 0.3.0; the earlier PyPI 0.2.0 release provides local stdio.

A push to main does not publish to PyPI or deploy AWS. Version-tag releases publish packages using PyPI Trusted Publishing. AWS deployment is an explicit manual operator action from 0.4.1. See CONTRIBUTING.md for releases and the AWS CI guide for deployment setup and limits.

For PyPI-based verification, the mcp-name marker above must match the name field in server.json.

What's Next

Phase 2 (session management, rich diagnostics, tool annotations, prompt registry, core.verify) is complete as of 0.2.0, and Phase 3 (3a AgenticLens, 3b Agentic Chaos, 3c AI Operations Specification, 3d Sidecar discovery) is complete as of 0.3.3. What's still open (see ROADMAP.md for full detail):

• Sidecar runtime controls: richer control surfaces require an authenticated, task-scoped runtime bridge
• Phase 5 (Publishing and Adoption) — publishing is automated; next is hosted access control, moved from Phase 4: tool-level permissions per key, revocation at dispatch, authenticated Tower/Sidecar stop/revoke, correlated denial evidence and hosted Tower credentials
• Control Tower coordination — local connector tools ship in 0.3.6 for Tower 0.7.0; hosted per-user credential binding remains planned
• Phase 6 — operational intelligence features

Development

A Makefile provides shorthand for common tasks:

make install     # install dev dependencies
make check       # run all quality gates (lint + format + typecheck + test)
make test-cov    # tests with coverage report
make docs        # regenerate docs/tools.md from tools/registry.py
make docs-check  # fail if docs/tools.md is out of date
make help        # list all available targets

Notes

This scaffold assumes the intended GitHub namespace is io.github.deepagentlabs/deep-agentic-core-mcp. If the final publishing account or org changes, update:

• the mcp-name marker in this README
• server.json
• any repository URLs in pyproject.toml

Operational safety roadmap

See planned safety acceptance criteria for responsibilities and dependencies. These additions do not claim implemented end-to-end containment or integrity protection.

Sidecar v0.7 compatibility

With Sidecar installed, sidecar.status detects its public decision, plan, runtime-event, audit and local operator APIs. runtime_ready means the decision API is installed; runtime_connected=false and mcp_runtime_controls=false make clear that these MCP discovery tools do not attach to an active task. sidecar.module_inventory lists adapter files, including placeholders; their presence does not certify framework support. No Sidecar dependency is required for MCP startup. An unavailable package produces an unavailable response.

For live task controls, use Sidecar's authenticated local OperatorServer/CLI interface described in its v0.7.0 live operations guide. Sidecar v0.7.0 is published separately. This patch improves discovery without enabling remote stop/revoke through MCP.

Sidecar 0.8 interoperability

MCP server 0.3.5 retains connectivity, discovery and transport ownership. Sidecar 0.8 adds a client adapter and a separate-process MCP 2.3.0 gateway example with server-side enforcement. This does not enable remote operator bridging in this server or expose shared/exclusive configuration through public tools.

Sidecar 0.8 provides Shared/Exclusive context-only, guardrails-only or combined configuration, explicit host context delivery and mandatory guardrail composition. Exclusive rules cannot weaken shared restrictions. Cross-framework privacy, revocation and delegated authority remain v0.9 validation; stable schemas remain v1.0. Native correlated Lens readback requires Lens 0.7.1. All sibling packages stay optional; no dependency floor is increased without a runtime requirement. See the Sidecar integration contract.

Control Tower 0.6.0 connector (MCP 0.3.6)

Local stdio exposes twelve tower.* tools over the actual authenticated Tower HTTP API: inventory/status/versions/posture/evidence, incidents, detection, investigation, preview, execute and verify. Configure AGENTICOPS_TOWER_URL (a trusted HTTP(S) origin), AGENTICOPS_TOWER_READ_TOKEN, and optionally the separate AGENTICOPS_TOWER_WRITE_TOKEN. No Tower package import is required. Without Tower, all other tools work; Tower tools return structured unavailable.

The tower.investigation prompt directs the host model to cite record IDs and artifact references, preserve uncertainty and missing evidence, recommend only the supported staging runbook, and leave approval to a human through Tower's HTTP/CLI. Approval credentials are never accepted by tool arguments or exposed through an approval tool. Execution is subject to Tower's exact-preview approval. The model provider remains the client's choice; none is bundled.

Hosted/remote MCP rejects Tower tools until per-user Tower credential binding exists. Local tokens cannot be borrowed by hosted tenants. Redirects are refused, responses are limited to 1 MiB and an isolated HTTP worker enforces a ten-second total deadline. A timeout may leave an action result unknown; inspect the Tower incident before retrying. URLs and tokens come only from trusted server environment. The Tower optional connector extra must use MCP >=0.3.6; other producer floors remain unchanged.

For setup, request JSON and both resolved/escalated native Lens/Evals walkthroughs, see Tower's connector guide and incident guide. Use tower.agents with status=unhealthy, tower.versions with an explicit minimum, or tower.evidence with source=agentic-sidecar to inspect high-risk posture without replacing native producer decisions.

Native Evals contract validation — MCP 0.3.8

Published Evals report and gate schemas (0.7.2 at MCP 0.3.8; 0.9.0 since MCP 0.3.14) are now enforced in the native consumer paths. Validation runs offline using bundled upstream contracts; invalid artifacts fail with field-level diagnostics. Native verdicts and AIOS conformance remain distinct. See behavior, examples and migration.

Agentic Evals 0.7.2 publishes native report (schema_version: "1.0") and gate-decision JSON Schemas, with offline report_schema() and gate_decision_schema() accessors. Contract 1.x evolves additively; readers should tolerate unknown fields. MCP spec.validate_artifact validates AIOS artifacts, not the native Evals schemas. Dedicated Evals tools and AIOS evaluation evidence export remain future work.

See the contract, examples and compatibility policy. Existing dependency ranges remain sufficient; using the new accessors directly requires agentic-evals>=0.7.2. This documentation alignment needs no sibling patch release.

AgenticLens 0.8.0 suite management

AgenticLens 0.8.0 delivers its v0.6 milestone: agenticlens.suites and agenticlens suite build regression suites from CSV/JSON imports, production traces and user feedback. It masks PII by default before storage (--no-mask opts out), detects duplicates and conflicting labels, adds synthetic variations and stable splits, records reviews, and approves suites into immutable, hash-verified versions that CI verifies, exports and evaluates. Suites stay plain agentic-evals TestSuite files.

MCP 0.3.9 adds two read-only, artifact-in/artifact-out tools: lens.suite_from_traces (draft cases from successful runs, PII masked by default, skipped runs with reasons) and lens.suite_check (summary, duplicates, unmasked-PII locations and the approval checks Lens would run). Neither stores anything; approval and the immutable store stay local CLI/API operations. Hosted images pin agenticlens==0.8.0.

See the suite management guide.

Evals 0.8.0 CLI interoperability

Evals 0.8.0 CLI JSON reports and gate decisions retain the 0.7.2 wire contract and validate with evals.validate_artifact. The bundled upstream 0.7.2 schemas and pinned contract-test dependency remain the compatibility baseline. JUnit and Markdown are presentation formats, not native validator inputs. This release adds no MCP execution tool or runtime dependency.

See CLI usage and compatibility.

Evals 0.9.0 judge reliability (MCP 0.3.14)

MCP 0.3.14 bundles the Evals 0.9.0 native report and gate schemas, so evals.validate_artifact enforces the new optional judge summary, live-target summary and per-case target_status fields. Validation stays offline and structural; MCP never recomputes a gate or treats an unavailable judgment as a pass. Hosted images pin agentic-evals==0.9.0. See the compatibility note.

Chaos 0.7.0 interoperability

Chaos 0.7.0 adds sequential fault cascades, deterministic monotonic breaking-point search, synthetic response poisoning (including prompt/tool-response instruction and policy-bypass payloads), and explicit-price retry cost estimates. Native child event IDs and fault types remain intact; cascade IDs/stages and poisoning strategy/payload/seed are additive event detail. Existing report readers and AIOS exports remain compatible.

See the v0.7 guide and offline synthetic example. The example includes observed synthetic dispatches and a bypassed negative control. Injection or recovery alone does not prove containment. No new shared schema, automatic adapter, hosted execution capability or containment guarantee is introduced.

MCP 0.3.10 pins published images to Chaos 0.7.0. The existing registry discovers poisoned_response; trusted local scripts can use cascade/search APIs. No new MCP tools are added. Hosted HTTP continues to reject chaos.run_experiment.

Control Tower 0.7.0 fleet operations

Tower 0.7.0 adds frozen environment/capability targeting, bulk desired settings, separate approval, durable per-target results and bounded stop/MCP-revocation acknowledgement through Python, HTTP and CLI. No fleet atomicity or automatic rollback/resume is claimed. Timed-out/requested commands need host reconciliation.

Local MCP 0.3.6 investigation tools remain compatible. Tower 0.7 bulk endpoints are Python/HTTP/CLI-only: MCP exposes no new fleet write or approval tools, and hosted Tower restrictions remain in force.

Existing optional dependency ranges remain sufficient. No sibling runtime or native evidence schema changes are required; this documentation alignment needs no patch release. See the operator guide.

See the local Tower compatibility guide for the operator workflow and package boundaries.

AgenticLens 0.9.0 ModelFit

AgenticLens 0.9.0 delivers its v0.7 milestone: agenticlens.modelfit and agenticlens modelfit recommend a model per task category from recorded experiment reports, under hard quality, cost, latency, safety, provider and deployment constraints that fail closed, with confidence, explanations, alternatives, provider-diverse fallbacks, savings tied to the experiment evidence, quality risks and a task-level model matrix.

MCP 0.3.11 adds the read-only lens.modelfit_recommend tool over supplied reports and pins hosted images to agenticlens==0.9.0. Running experiments stays a local agenticlens experiment run step; ModelFit recommends and does not route traffic.

See the ModelFit guide.

AgenticLens 0.10.0 routing (MCP 0.3.12)

lens.routing_decide explains a route from a supplied ModelFit report, request (request_id, category_id) and optional routing policy. It returns constraints, confidence, ranked targets, fallback chain and policy/report hashes. It is read-only in every mode, including enforce: no model invocation, callbacks, human approval execution or audit-file writes. executed is always false. An unrouteable decision stays blocked/escalated in the native result. Older Lens installs degrade only this tool with an explicit unavailable error.

{
  "report": {"...": "supply the complete ModelFit report"},
  "request": {"request_id": "opaque-123", "category_id": "orders"},
  "policy": {"minimum_confidence": "medium"}
}

Hosted images pin agenticlens==0.10.0 and retain agentic-chaos==0.7.0. The workspace source takes precedence over stale copies in sibling virtualenvs. See the Lens routing guide for callback execution and audit boundaries. Sidecar authority, Tower approvals, Evals scoring, Chaos faults and AIOS shared schemas remain independently owned.

Chaos 0.8.0 interoperability

Chaos 0.8.0 adds:

• multi-stage chaos workflows: steps, steady-state health checks, parallel branches, approval gates that can be resumed, and reusable templates
• declarative YAML/JSON/TOML experiments, with validation that never executes targets and cron schedule previews
• Scope explosion-radius control (steps, providers and exclusions) on every built-in fault and cascade(); custom faults get it by calling BaseFault.__init__, setting scope or overriding in_scope()
• a topology fuzzer that weights fault placement by fan-out

Native event IDs and fault types are unchanged. Workflow provenance (detail.workflow) and an optional ChaosReport.workflow summary are additive. A workflow's steps merge into one native report for contracts and AIOS export.

See the v0.8 guide and the offline examples. Workflow statuses, health checks and fuzz outcomes are resilience evidence. They are not scores, release gates or proof of containment. No new shared schema, automatic adapter or hosted execution capability is introduced.

MCP 0.3.13 pins the published images to Chaos 0.8.0. chaos.export_aios_run carries the workflow summary of merged reports in the producer extension, and the existing registry discovers faults. Workflows, experiments and fuzzing are Python/CLI orchestration, not new MCP tools. Hosted HTTP continues to reject chaos.run_experiment, so no experiment target is executed by the hosted service.

Sidecar 0.9.0 interoperability

Task-authority/delegation and reader-safe summaries are available in Sidecar. See compatibility and ownership boundaries. Existing native decisions/correlation stay compatible; no new normative schema is claimed.