Multi-agent orchestration for work that must reach a verified end state
Long context agentic coordination | End-state reasoning | Integrated tool calling | Durable self-correction and self-improvement
Nomos turns broad objectives into coordinated, evidence-backed operations. It is
built for work that crosses people, agents, tools, repositories, documents, and
long-running processes, where a useful system must do more than produce a draft.
Nomos works backward from the required end state. It resolves intent, identifies
missing evidence and operational dependencies, coordinates independent work in
parallel, reviews actions before execution, learns from observed outcomes, and
keeps the objective open until the real result is validated.
From AI assistants to coordinated operations
Most assistants optimize one response. Most automation platforms require a human
to define every step in advance. Nomos sits between those approaches: the model
owns a dynamic objective graph while the runtime provides durable execution,
identity, evidence, correction, and continuation boundaries.
Business need
Nomos capability
Operational result
AI transformation
Converts open-ended requests into model-owned objective graphs, actions, evidence, and verified completion
Move from chat-only pilots to delegated work that can be reviewed, resumed, and measured
Resume work after process restarts or handoffs without rebuilding context from a summary
Engineering execution
Connects repository inspection, reproduction, repair, tests, regression review, and handoff
Move from issue report to verified change in one continuous evidence chain
Research and analysis
Coordinates source collection, experiments, comparison, challenge, synthesis, and disclosure
Produce decisions that retain their evidence trail instead of collapsing research into an unsupported answer
Operational recovery
Uses current state, runbooks, tools, confidence, and validation edges to adapt when the environment changes
Reduce repeated failure loops and keep recovery tied to observed system state
Agent platform consolidation
Exposes one orchestration runtime through CLI, HTTP, OpenAI-compatible tools, MCP, A2A, and Docker
Give multiple applications and model clients a common long-horizon operating layer
Continuous capability improvement
Correlates verified outcomes with the exact model route, reviewer, workers, and operational state that produced them
Improve future execution without mixing users, projects, or unrelated task state
Governed autonomy
Applies schemas, authentication, workspace containment, signed observations, approvals, and evidence-bound completion
Increase autonomy while retaining operational control and auditability
What makes Nomos different
Graph end-state reasoning
Nomos begins with the state that must be true when the work is finished. It then
works backward through intent, confidence, operations, evidence, dependencies,
and validation. The graph can expand when execution discovers a new requirement;
it is not constrained to a static checklist or a fixed number of reasoning steps.
Native multi-agent coordination
Worker roles are capabilities in the active graph, not host-authored role-play.
Nomos can coordinate independent work concurrently and pass actual evidence
identities to dependent workers.
Common work products include:
Drafting for candidate actions, plans, responses, and disclosures.
Research for source discovery, comparison, and synthesis.
Reproduction for isolating failures across environments and variants.
Experimentation for resolving uncertain causal edges.
Triage for impact, priority, ownership, and competing hypotheses.
Patching for minimal evidence-grounded repairs.
Validation for regression checks, edge cases, and end-state proof.
Disclosure for clear user, customer, operational, or executive reporting.
Coordination for worker identity, dependencies, knowledge exchange, and reconciliation.
Nomos records both observed exchanges and learned knowledge transfer. This allows
one worker's verified finding to alter later drafting, action selection, and
validation without reducing the transfer to an unverified summary.
Integrated tool calling
Tool calling ships inside every complete Nomos profile. The action component is
integrated under the profile's model authority and is not a separate product,
service, or download.
The integrated action capability proposes semantically relevant function calls
and structured arguments. Nomos remains responsible for objective intent,
worker coordination, graph fit, confidence, action review, execution authority,
observation handling, correction, and final completion.
A failed action is not merely stored for a future session. Nomos binds the signed
observation to the exact call, draft, worker, graph edge, and producing route.
The active task can then revise arguments, choose another capability, acquire
missing evidence, reopen an uncertain decision, or produce a new reviewed draft.
Correction is candidate-specific. A denied action in a parallel batch does not
indiscriminately degrade approved sibling actions, and an outcome cannot redirect
credit to an unrelated draft.
Verified self-improvement
Nomos improves from real outcomes rather than from ungrounded critique. Verified
success or failure can update the exact surfaces that produced the behavior:
action and response reviewers;
recurrent confidence and correction routes;
candidate and operational representations;
NoNE expert participation and cross-capability transfer;
model-owned task, traversal, and coordination state;
worker dependencies and knowledge exchange;
native proposal fidelity;
learned reliability for operational capabilities.
If an initial attempt fails, Nomos can correct inside the current task. If a
corrected attempt also fails, it can persist improvement before trying again.
Learning is session-owned, cold-loadable, and correlated with the producing
state, so delayed feedback cannot borrow an unrelated live task.
Long-context, long-horizon continuity
Nomos is configured for a native context window of 4,194,304 tokens. Long context
supports large repositories, document collections, logs, histories, and ongoing
projects, while durable task state supports work that outlives one context or one
server process.
Context processing remains connected to attention, KV state, objective state,
intent, action state, NoNE participation, model-owned coordination, and recurrent
confidence. Native live analysis can expose token progress and model-owned
completion state without replacing the answer with a host-selected shortcut.
Industries and operating teams
Software and platform engineering
Use Nomos to investigate incidents, inspect repositories, reproduce defects,
compare repair strategies, implement changes, execute tests, review regressions,
and create evidence-linked handoffs. Multiple workers can investigate in
parallel while dependent patching and validation wait for grounded findings.
IT, security, and operations
Coordinate state inspection, runbook execution, incident triage, remediation,
validation, and status reporting. Nomos can adapt when live state differs from a
stale plan and preserve who requested an action, what executed, and which
observation changed the response.
Finance and regulated operations
Coordinate document review, policy checks, reconciliations, exception handling,
and evidence packages across multiple systems. Session isolation and signed
observations make it easier to retain the operational chain behind a decision.
Healthcare administration and life-science operations
Support high-context administrative and research workflows that combine records,
procedures, evidence collection, review, and follow-up. Nomos is suited to
coordinating operational work and human review; deployments remain responsible
for domain-specific authorization and safety controls.
Manufacturing and field operations
Connect telemetry checks, maintenance evidence, incident reproduction, repair
planning, validation, and status consolidation. Failed operations reduce learned
capability confidence instead of being misreported as successful merely because
they ran.
Research, legal, and professional services
Run parallel source collection, fact comparison, challenge, drafting, review,
and disclosure. The objective graph preserves unresolved questions and evidence
dependencies until the final work product is ready for its intended audience.
Case state, tool-backed actions, exception correction, escalation evidence, and consistent final communication
Compliance workflow
Structured operations, signed observations, review steps, session isolation, and durable receipts
Model-fleet orchestrator
OpenAI-compatible, MCP, A2A, HTTP, and CLI boundaries around one persistent objective and worker graph
Persistent workspace agent
Repository and file operations, resumable tasks, artifacts, corrections, learned outcomes, and cold continuation
Tool-rich application backend
Integrated function-call candidates plus Nomos intent, confidence, review, execution, and follow-up orchestration
Continuous improvement system
Outcome-correlated learning for routes, reviewers, workers, action candidates, and capability reliability
What you can build
Autonomous engineering teams that move from report to verified patch.
Research agents that split an investigation, reconcile evidence, and retain provenance.
Incident-response coordinators that adapt to changing infrastructure state.
Persistent project agents that resume after interruption with the same objective graph.
Internal operations agents that coordinate tools, approvals, artifacts, and human input.
Agent platforms that expose one durable orchestration layer to multiple clients and models.
Review and disclosure workflows that connect the final document to the work that produced it.
Self-correcting tool workflows that learn from authenticated execution outcomes.
Choose your Nomos
Nomos uses a complete-profile family structure. A profile is a
standalone model, runtime, server, CLI, Docker definition, and operating guide.
Profiles are never hand-selected tensor subsets.
model/components/tool_calling/ is an internal part of the Nomos model profile.
It is loaded, reviewed, and governed by Nomos and is not an independent release.
Nomos provides an authenticated OpenAI-compatible chat and tool boundary while
preserving its own objective, worker, action, correction, and learning state.
With no command override, the image runs the server. Supplying arguments runs
the CLI:
bash
1docker run --rm --gpus all \2 -e NOMOS_API_KEY=replace-me \3 -p 8000:8000 \4 namenotfoundai/nomos:universal
56docker run --rm --gpus all \7 namenotfoundai/nomos:universal doctor
Security and operational control
Nomos includes authenticated service boundaries, session namespaces, strict tool
schemas, workspace containment, signed observations, call correlation, secret
redaction, durable receipts, and exact outcome association. Organizations remain
responsible for network policy, identity integration, tool permissions, human
approval rules, and domain-specific safeguards around deployed actions.
Citation
bibtex
1@software{nomos2026,
2 title = {Nomos: Multi-Agent Graph End-State Orchestration},
3 author = {Name Not Found AI},
4 year = {2026},
5 url = {https://huggingface.co/namenotfoundai/Nomos}
6}
