Featuring research from
What Leading Analysts Are Saying
Context graphs are the infrastructure. Domain expertise is the fuel. Operational trust is the unlock. Three converging trends identified by Gartner point to the same conclusion — and to what Interloom is built to do.
Context Graphs Are the New Essential Infrastructure for Agentic Systems
By Radu Miclaus, Tom Coshow, et al.
Key Finding
Context graphs track not only data context but also "decision flows" and event traces, enabling more informed and effective business decision making by AI agents.
Strategic Planning Assumption
By 2028, over 50% of AI agent systems will leverage context graphs, driven by the need for increased domain specificity and operational grounding.
Recommendation
Extend context engineering beyond traditional vectorization and knowledge graphs by capturing "decision workflows" and business logic to deliver high-quality domain-specific agentic systems.
Gartner positions context graphs as the next evolution beyond knowledge graphs — capturing not just "what" and "who" through static entities, but "how" and "why" through decision traces and procedural logic that emerges from actual execution.
The report identifies five concrete benefits: capturing domain-specific organizational memory, differentiating in crowded AI markets, enabling compliance through "immutable audit trails," accelerating enterprise sales through clear ROI, and reducing implementation cost through domain-specific accelerators.
How Interloom Delivers on This
Organizational Memory by Design
Interloom's context graph is exactly the "persistent enterprise memory layer" Gartner describes. Every case an expert resolves becomes a precedent — a decision trace that captures not just what was decided, but the reasoning, context, and outcome.
Knowledge That Emerges from Execution
Gartner emphasizes that context graphs should emerge from real operational data — emails, tickets, transcripts. Interloom does exactly this: it extracts operational expertise hidden in communication channels and transforms it into structured decision traces. The graph grows organically from how your team actually works.
Compounding Decision Intelligence
Every resolved case adds institutional memory that becomes "increasingly hard to replace." Agents improve because the graph grows deeper — not because prompts are manually rewritten. Over time, this creates a durable competitive moat for every customer.
Decision Lineage and Audit Trails
Every agent action traces back to the operational precedent that grounded it — providing the decision lineage, auditability, and governance that regulated industries require.
Source: Gartner, "Emerging Tech: AI Vendor Race: The New Essential Infrastructure for Agentic Systems: How Context Graphs Are Solving AI's Institutional Memory Problem," 13 February 2026. ID: G00846665.
Gartner · January 2026 · G00825303
Innovation Guide for AI Agents
By Arun Chandrasekaran, Leinar Ramos, et al.
Adoption
17%
of respondents to the 2026 Gartner CIO Survey had already deployed AI agents; 42% said they would do so within 12 months.
Key Trend
Domain-specific agents in financial services, logistics, and customer operations are outpacing generic horizontal platforms.
Enterprise Blocker
"Governance, control, and safety" are the primary barriers to scaled adoption, alongside gaps in persistent memory and workflow integration.
Gartner maps a clear market evolution: enterprises are moving beyond chat interfaces toward agents that perform tasks end-to-end. But agents require "persistent memory, dynamic planning," and mature orchestration — and must be observable, auditable, and controllable before enterprises will move beyond pilots.
How Interloom Delivers on This
Domain-Specific by Architecture
Interloom is not a generic agent builder that customers must customize — it is purpose-built for operational domains like facility management, insurance, and banking. The context graph captures domain-specific procedural knowledge from day one.
Governance by Experts, Not Just Engineers
Subject matter experts — the people who actually run operations — define governance rules, workflows, and escalation paths in natural language. The people with the deepest domain knowledge control how agents behave.
Persistent Memory as a Core Primitive
Operational memory is not an add-on module — it is the foundation. The context graph serves as persistent, evolving memory that agents query for grounding, and that grows richer with every case resolution.
Source: Gartner, "Innovation Guide for AI Agents," 22 January 2026. ID: G00825303.
Gartner · February 2026 · G00846839
Demystifying Agent Operations in the Evolving AI Engineering Landscape
By Haritha Khandabattu, Arun Chandrasekaran, et al.
Core Argument
Most organizations "overinvest in agent construction and underinvest in agent operations." The result: agents that work in demos but fail at scale.
Key Insight
"Trust in agents is no longer established at deployment. It must be continuously earned through evaluation, traceability, and the ability to intervene."
The primary risk is not catastrophic failure but "silent degradation" — agents that continue functioning while subtly drifting from expected behavior. Gartner's prescription: "progressively expand agent autonomy" based on production evidence, not upfront assumptions.
How Interloom Delivers on This
Trust Built into the Architecture
Agents can only act on what the context graph has grounded. They do not improvise from generic training data — they execute based on operational precedents that experts have validated.
Progressive Autonomy through Expert Feedback
As experts resolve more cases, the context graph deepens, and agents gain more precedents. Autonomy expands organically because the grounding improves. When the graph lacks precedent, the agent escalates rather than guessing.
Observability across Every Decision
Every agent action traces to a specific precedent in the context graph. If behavior changes, it is because the underlying operational knowledge changed — and that change is visible, auditable, and attributable.
Source: Gartner, "Demystifying Agent Operations in the Evolving AI Engineering Landscape," 22 February 2026. ID: G00846839.
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The Convergence
These three research threads point to the same conclusion. The next generation of enterprise AI will be defined not by model capabilities alone, but by the operational context that grounds them.
Generic agents built on generic knowledge will hit a ceiling. The organizations that win will be the ones that capture their institutional expertise as a compounding asset — where every case resolved makes the next one faster, more accurate, and more autonomous.
That is what Interloom is built to do.
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