AI AND AGENTIC SYSTEMS
AI, Agentic & Knowledge Systems
Use the right model at the right boundary - with authority, memory, and verification outside the model.
Design intelligence systems where models propose, tools execute under authorization, retrieval grounds answers, and evaluation preserves human control across engineering and knowledge workflows.
This is an engineering domain describing how Mythos Systems approaches AI and agentic work - not a claim that every model integration is production-ready.
Intent
What this solution is for
Approaches for model routing, agent coordination, retrieval grounding, evaluation, and knowledge systems - with explicit boundaries between probabilistic intelligence and deterministic authority.
Technology Horizon
Editorial indicators
Verified 2026-07-17. Not a product rating or readiness score.
Technology Horizon scores are Mythos Systems editorial indicators reflecting current market maturity, emerging technical dependencies, integration difficulty, and architectural differentiation. They are not third-party benchmarks or product-readiness certifications.
Problem landscape
Where operating models break down
- Models are added to products without clear boundaries for authority, permissions, or verification.
- Retrieval pipelines often lack provenance, freshness controls, or citation discipline.
- Agent frameworks enable tool use but rarely enforce budgets, stopping conditions, or audit trails.
- Local, hosted, and browser-local deployment options are chosen ad hoc without privacy analysis.
- Evaluation covers demos - not factuality, grounding, tool selection, or injection resistance.
- Memory systems conflate conversation history with authoritative engineering state.
- Research patterns from experiments are hard to trace into product architecture decisions.
- Public assistants risk overclaiming capabilities without route citations and corpus boundaries.
What Mythos changes
Architectural shift
- Intent flows through source selection, planning, model routing, and authorization before execution.
- Retrieval is designed to ground answers in approved corpus with explicit citation paths.
- Agents operate with permissions, budgets, and stopping conditions - not unbounded tool access.
- Evaluation is treated as a continuous requirement, not a one-time benchmark.
- Deterministic systems retain authority over state, permissions, validation, and execution.
Operating model
Target lifecycle
A proposed control loop for this domain - not a claim that every step is shipped.
- INTENT
- SOURCE AND CONTEXT
- PLAN
- SELECT MODEL
- ASSIGN AGENT
- AUTHORIZE TOOL
- EXECUTE
- EVALUATE
- VERIFY
- RECORD
- IMPROVE
INTENT → SOURCE AND CONTEXT → PLAN → SELECT MODEL → ASSIGN AGENT → AUTHORIZE TOOL → EXECUTE → EVALUATE → VERIFY → RECORD → IMPROVE
Capability architecture
Capability pillars
Expand a pillar for purpose, functions, and boundaries.
Model Routing & Profiles Details
Select hosted, local, open-weight, or browser-local models based on task, privacy, and cost.
Includes
- Hardware-aware model recommendations
- Provider and deployment profile management
- Cost and latency transparency for routing decisions
- Conservative escalation to frontier models when policy permits
Outputs
- Model routing decisions with rationale
- Usage and cost summaries
Retrieval & Grounding Details
Ground answers in approved corpus with provenance and citation discipline.
Includes
- Corpus indexing with freshness and scope controls
- Route-aware retrieval for public site knowledge
- Source-linked citation in assistant responses
- Grounding evaluation against approved content boundaries
Boundaries
- Does not mean unrestricted web search without corpus approval
Agent Coordination Details
Coordinate multi-step agent workflows with explicit permissions and observability.
Includes
- Tool assignment with authorization gates
- Parallel and sequential agent task graphs
- Budget, timeout, and stopping-condition enforcement
- Agent activity observability and replay candidates
Memory & Knowledge Details
Preserve durable engineering memory separate from ephemeral conversation state.
Includes
- Shared memory across sessions with scope controls
- Source-linked documentation and research integration
- Distinction between conversational context and authoritative records
- Knowledge graph and corpus relationship candidates
Evaluation & Safety Details
Evaluate model and agent behavior against factuality, grounding, and safety criteria.
Includes
- Factuality and grounding evaluation workflows
- Tool-selection and permission-boundary testing
- Prompt injection resistance evaluation candidates
- Continuous improvement loops with recorded outcomes
Outputs
- Evaluation reports
- Regression test candidates
Public Assistant Boundaries Details
Provide constrained public intelligence with route citations and corpus limits.
Includes
- Ask Mythos retrieval over approved public corpus
- Route citation and scope limitation in responses
- Optional on-device intelligence with explicit consent
- Clear disclosure of model and retrieval boundaries
Boundaries
- Does not mean the public assistant replaces product-specific engineering tools
Solution ecosystem map
Systems, standards, and references
Browse Mythos systems, protocols, open-source candidates, and market references for this solution. Named external tools are references or integration candidates unless a stronger relationship is labeled.
Canonical ecosystem for this solution. Mythos products are classified as Mythos systems — never as external dependencies. Protocols and market tools are references or candidates unless a stronger relationship is labeled.
- Total records
- 21
- Mythos systems
- 7
- Protocols & standards
- 1
- Open-source references
- 1
- Candidate integrations
- 11
- Verified / documented deps
- 0
- Research & lineage
- 2
Use the tabs above to browse categories, or search and filter the full map.
21 results
Automation & Orchestration 3
- Durable Mission Orchestration Mythos Subsystem
Moirai runtime for durable multi-agent and workflow execution.
Architecture reference
- LOGOS Blueprints Mythos Subsystem
Typed directive language compiling human intent into Mission Blueprints.
Architecture reference
- Python Automation Candidate
Python tooling for parsers, labs, and integration adapters.
Architecture reference
Data Models & Source of Truth 1
- JSON Schema Candidate
Schema validation for normalized internal and API payloads.
Architecture reference
Data & Storage 2
- PostgreSQL(opens in new tab) Candidate
Shared relational state for enterprise control planes.
Architecture reference
- Search Indexes Candidate
Full-text and semantic indexes for ops knowledge.
Architecture reference
Mythos Systems 5
- AEON Mythos System
Private life operating system - privacy-first household and personal coordination.
Architecture reference
- DELPHI Mythos Subsystem
Knowledge, research, documentation, and architecture records.
Architecture reference
- PANTHEON Mythos System
Natural-language AI engineering and governed automation platform.
Architecture reference
- PROTEUS Mythos Subsystem
Hardware-aware model routing and local/cloud model integrations.
Architecture reference
- VULCAN Mythos Subsystem
Creation and automation workflow from concept to LOGOS Mission Blueprint.
Architecture reference
Open-Source References 1
- LangGraph(opens in new tab) Candidate
Graph-based agent workflow patterns.
Architecture reference
Protocols & Standards 1
- NIST AI RMF(opens in new tab) Market Reference
Risk management framework for AI systems.
Architecture reference
Research & Lineage 2
- MYTHOS AI Research
Applied AI research lineage for models, retrieval, agents, and orchestration.
Architecture reference
- PRISMATIK Research
Evidence-chained market intelligence architecture - distinct from PRISM.
Architecture reference
Runtimes & Interfaces 4
- Rust Candidate
Systems language for Mythos desktop and service cores.
Architecture reference
- TypeScript Candidate
Typed UI and tooling language.
Architecture reference
- Web Workers Candidate
Off-main-thread work for local inference and parsing.
Architecture reference
- WebLLM Candidate
Browser-local inference for constrained assistants.
Architecture reference
Security & Identity 1
- Cedar(opens in new tab) Candidate
Policy-as-code engine direction for capability grants.
Architecture reference
Telemetry & Observability 1
- OpenTelemetry(opens in new tab) Candidate
Vendor-neutral traces, metrics, and logs correlation.
Architecture reference
Use cases
By environment
Engineering teams
- Route models by task sensitivity, cost, and hardware constraints
- Coordinate multi-step agent missions with authorization and verification gates
Knowledge and documentation
- Ground public assistant answers in approved site corpus with citations
- Link research artifacts to product architecture decisions
AI research and evaluation
- Evaluate factuality, grounding, and tool-selection behavior systematically
- Test prompt injection resistance against approved corpus boundaries
Privacy-sensitive workloads
- Select local or browser-local models when data policy requires on-device processing
- Document model provider and data-flow boundaries for review
Public site intelligence
- Provide Ask Mythos retrieval over approved public content
- Offer optional on-device intelligence with explicit user consent
Deliverables
Potential outcomes
Artifacts an engagement or future platform workflow could produce.
- Model routing profiles with privacy and cost rationale
- Retrieval pipeline designs with corpus scope and citation rules
- Agent mission specifications with permission and budget boundaries
- Evaluation frameworks for factuality, grounding, and tool selection
- Public assistant corpus manifests and route citation maps
- Research-to-product traceability records for intelligence patterns
- Risk assessment candidates aligned to NIST AI RMF and OWASP GenAI
- Improvement loops with recorded evaluation outcomes
Controls & boundaries
What this does not mean
- Does not mean every product includes autonomous agents
- Does not mean models hold authority over permissions, state, or execution
- Does not mean MYTHOSAI is a production autonomous platform
- Does not mean public assistant answers replace product-specific engineering tools
- Does not mean partnership with any model provider or framework vendor listed
- Does not mean published benchmark scores without documented methodology
Resources
Related library material
Mythos principles for model deployment, routing, agents, and control boundaries.
Applied AI research lineage including MYTHOSAI experimentation patterns.
Research notes, comparisons, and architectural explorations for AI systems.
Original Mythos standards for AI evaluation and retrieval methodology.