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ARXIV:2603.14805 · KNOWLEDGE MANAGEMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.14805KNOWLEDGE MANAGEMENTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development.
Opportunity summary
Pain A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development. The bottleneck to effective agentic software development is not model capability but knowledge architecture.
Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation. The bottleneck to effective agentic software…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. When any knowledge consumer - an autonomous AI agent, a newly onboarded engineer, or a senior developer - encounters an enterprise task without institutional…
Knowledge Management moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development.
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10.48550/arXiv.2603.14805A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development.
Abstract
Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation. The bottleneck to effective agentic software development is not model capability but knowledge architecture. When any knowledge consumer - an autonomous AI agent, a newly onboarded engineer, or a senior developer - encounters an enterprise task without institutional context, the result is guesswork, correction cascades, and a disproportionate tax on senior engineers who must manually supply what others cannot infer. This paper introduces Knowledge Activation, a framework that specializes AI Skills - the open standard for agent-consumable knowledge - into structured, governance-aware Atomic Knowledge Units (AKUs) for institutional knowledge delivery. Rather than retrieving documents for interpretation, AKUs deliver action - ready specifications encoding what to do, which tools to use, what constraints to respect, and where to go next - so that agents act correctly and engineers receive institutionally grounded guidance without reconstructing organizational context from scratch. AKUs form a composable knowledge graph that agents traverse at runtime - compressing onboarding, reducing cross - team friction, and eliminating correction cascades. The paper formalizes the resource constraints that make this architecture necessary, specifies the AKU schema and deployment architecture, and grounds long - term maintenance in knowledge commons practice. Organizations that architect their institutional knowledge for the agentic era will outperform those that invest solely in model capability.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development. The bottleneck to effective agentic software development is not model capability but knowledge architecture.
METHOD
Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation. The bottleneck to effective...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. When any knowledge consumer - an autonomous AI agent, a newly onboarded engineer, or a senior developer - encounters an enterprise task without institutional context, the result is guesswork, correction c...
WHY NOW
Knowledge Management moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development. The bottleneck to effective agentic software development is not model capability but knowledge architecture.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Enterprise software organizations accumulate critical institutional knowledge - architectural decisions, deployment procedures, compliance policies, incident playbooks - yet this knowledge remains trapped in formats designed for human interpretation. The bottleneck to effective agentic software development is not model capability but knowledge architecture.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. When any knowledge consumer - an autonomous AI agent, a newly onboarded engineer, or a senior developer - encounters an enterprise task without institutional context, the result is guesswork, correction cascades, and a disproportionate tax on senior engineers who must manually supply what others cannot infer.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Knowledge Management moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A framework for transforming institutional knowledge into actionable specifications for AI agents in enterprise software development.
Segment
Knowledge Management
Adoption evidence
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Commercial read
3.0/10 public viability
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missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Evidence coverage
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stale
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Defensibility
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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Prototype owner missing.
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Operator workflow not sourced.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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FORESIGHT
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