Career Highways CEO Liz Eversoll joins The TechEd Podcast to discuss how organizations can move beyond traditional job architectures and embrace skills intelligence. The conversation explores AI, workforce transformation, and practical strategies for building more agile, skills-driven talent ecosystems.
As enterprises move from chat assistants to production agents, the real constraint isn’t model intelligence — it’s the context window, and it’s denominated in tokens. This piece traces why: how text becomes tokens, why the key-value cache makes those tokens the true driver of cost, latency, and memory, and why the format you choose to represent knowledge is both an efficiency and a reasoning-quality decision. It introduces SIGN, Career Highways’ open notation, which cut a ~200-document canon by roughly a quarter versus markdown — with the deepest savings in the most governed content.
Career Highways has released SIGN™ (Sigil Intelligence Graph Notation), a new open standard that turns enterprise policies, rules, and definitions into a structured format AI agents can read, audit, and act on. The company has also filed patent applications related to the technology and is making SIGN™ freely available under the MIT License.
As businesses move AI agents into real operations, a gap keeps appearing: agents can find information but struggle to apply company rules, respect constraints, or explain their decisions. SIGN™ closes that gap with a compact, token-efficient knowledge layer between enterprise systems and AI agents — one that can reduce representation overhead by up to 50% compared with formats like JSON, while keeping decisions governed and traceable.
SIGN™ lets organizations capture five things agents need: facts, rules, constraints, inference patterns, and provenance. The result is knowledge that’s structured, enforceable, versioned, and auditable — so an agent’s output can be traced back to the exact rule, source, and version behind it.
“SIGN™ gives organizations a governed way to declare what they know, what rules apply and what an agent is allowed to do with that knowledge,” said Liz Eversoll, CEO of Career Highways. “It is the missing contract layer between enterprise knowledge and agentic AI.”
Documentation, examples, and the codebase are available now on GitHub. Learn more at careerhighways.com.
New token-efficient format can cut representation overhead by up to 50% while keeping AI decisions governed, versioned, and traceable.
MADISON, WI — July 14, 2026 — Career Highways today unveiled SIGN (Sigil Intelligence Graph Notation) and confirmed it has filed patent applications tied to the technology. SIGN is an open standard that lets organizations translate their policies, rules, definitions, constraints, and provenance into a structured form that AI agents can read, verify, and act on. The company is publishing SIGN under the MIT License.
The launch targets a problem that surfaces as companies push AI agents from pilots into live operations: agents are good at retrieving information but unreliable at applying an organization’s actual rules, respecting its limits, or explaining the reasoning behind a decision. The underlying knowledge usually exists already — scattered across documents, systems, and governance workflows — but not in a shape agents can use consistently. SIGN sits between enterprise systems and AI agents as a compact, structured knowledge layer that fills that gap.
“AI agents are moving into production faster than most organizations can govern the knowledge those agents depend on necessitating a governance framework they can operate with,” said Liz Eversoll, CEO of Career Highways. “SIGN gives organizations a governed way to declare what they know, what rules apply and what an agent is allowed to do with that knowledge. It is the missing contract layer between enterprise knowledge and agentic AI.”
Career Highways frames SIGN as doing for enterprise knowledge what SQL did for structured data: providing a shared language. Where documents are human-readable but not enforceable, and JSON serializes data without conveying meaning, SIGN is purpose-built for agent reasoning. It lets organizations express five things — facts (definitions, properties, relationships, and domain knowledge), rules (the logic agents apply to decisions), constraints (the boundaries agents must stay within), inference patterns (when agents may draw new conclusions), and provenance (the source, version, and authority behind a piece of knowledge).
The company points to common failure modes that pure retrieval doesn’t solve: a customer-service agent locating the right policy but botching an exception, an HR agent surfacing career-pathway details while missing eligibility rules, a compliance agent citing a regulation but not the current version, or a workflow agent making a recommendation without showing which rule justified it. SIGN is built around three capabilities meant to address these — reasoning-ready knowledge that encodes rules and inference patterns rather than just generating text; governed decisioning through versioning, provenance, and auditability so outcomes trace back to a specific rule and source; and token efficiency that fits more governed knowledge into an agent’s context window at lower cost.
Releasing SIGN openly is a deliberate bet. “Foundational infrastructure wins when it is open,” Eversoll said. “SQL, HTTP and OpenAPI became durable because organizations could adopt them without locking themselves into one vendor. We believe the knowledge layer for AI agents needs that same openness.” Alongside the open standard, Career Highways plans to offer enterprise-grade infrastructure around SIGN — including registry, namespace, validation, audit, and governance systems — for teams deploying agentic AI at scale.
Documentation, examples, and implementation guidance are available now, and developers can access the codebase on GitHub. Learn more at careerhighways.com.
About Career Highways
Career Highways is a workforce strategy and technology company that helps large, complex organizations design and activate transparent, skills-based career pathways at enterprise scale. Its services and tools — including Skills Intelligence — digitize job architecture, map skills to roles, and turn workforce data into clear pathways for mobility, upskilling, and planning. By pairing AI-enabled insight with human expertise, the company supports better decisions about talent development, internal movement, and the changing impact of technology on work.
Media Contact: Philip Robertson, Impact Partners PR LLC
For thirty years, durable software was the only path to the outcomes organizations wanted — so we built it, maintained it, migrated it, and kept feeding it. Most enterprises now spend 60–80% of their IT budgets just sustaining systems that already exist, leaving twenty cents on the dollar for anything new. The machine developed an appetite — and a whole services ecosystem to keep it fed.
AI is being sold as a way to build software faster. That’s true, and it’s the least interesting part. The real shift: when AI can assemble software on demand to serve a specific outcome and dissolve it when the moment passes, software stops needing to be durable. It becomes temporal — built for the moment, gone when the moment ends. The question that’s haunted every technology budget for thirty years finally has an answer: it stops when the moment passes.
But temporal software needs a venue — a durable, governed foundation of organizational knowledge, ontology, and intelligent infrastructure that every temporary application inherits. Most organizations are deploying AI without one. They’re booking events without a venue. The engineers who matter most in this model aren’t building applications — they’re building the venue. We call them intent engineers.
