AI Engineer · Published 2026-10-09

Why Your Company Needs a Context Graph (and How to Build It) — Gil Feig, Merge

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Context GraphAI agentscachingprovenanceMCPcontext-graphagent-memoryretrieval-routingcontext-tiersenterprise-ai
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Summary

Overview

  • Speaker: Gil Feig
  • Channel: AI Engineer
  • Main topic: Enterprise AI infrastructure and the necessity of context graphs for production AI applications
  • Purpose: To explain what a context graph is, why companies need one beyond simple MCP servers or live API lookups, and how to architect and build a robust, traceable context layer for enterprise AI agents. Gil Feig, co-founder and CTO of Merge, explains why companies need a context graph to power production AI applications effectively. He breaks down the components of a company context graph, including third-party data, structured context, memories, and skills. He outlines the tiers of context—prompt/skills/memory, live API calls, cached data, and derived context—and discusses selection strategies, syncing layers, routing, and provenance requirements for reliable enterprise AI systems.

Topic Map

Introduction to Merge and Context Graphs

  • Explanation: Gil Feig introduces Merge, a connective infrastructure platform for production AI, and sets the agenda for discussing why companies need a context graph and how to build one.
  • Key claims:
    • Merge provides unified, agent handler, and gateway infrastructure for enterprise AI.
    • Simple MCP server connections are not enough for complex semantic queries across large company data sets.
  • Examples:
    • Connecting to Zendesk or Salesforce.
  • Terminology:
    • Merge
    • context graph
    • MCP server
    • agent handler
    • gateway
  • Why it matters: Establishes the foundational need for robust enterprise AI infrastructure beyond simple API wrappers.

What is a Context Graph

  • Explanation: Defines the company context graph as the company's brain, encompassing third-party data, structured context, memories, and skills rather than literal node-and-edge graph databases.
  • Key claims:
    • A context graph consists of third-party data, structured context, memories, and skills.
    • Skills are a vital part of a context graph.
  • Examples:
    • Using NetSuite, Jira, and static documents as data sources.
  • Terminology:
    • third-party data
    • structured context
    • memories
    • skills
  • Why it matters: Provides a mental model for how AI agents ingest and process organizational knowledge.

The Limitations of Live Lookups and MCP

  • Explanation: Demonstrates through an example why live API lookups (like querying Zendesk for customer tickets one by one) fail when scaling to broader questions like finding all upset customers over a year.
  • Key claims:
    • Live lookup methodology in platforms like MCP does not support live aggregation across vast datasets.
    • Platforms do not incentivize vectorizing data due to cost and maintenance.
  • Examples:
    • Asking 'Why was customer A upset last week?' vs 'Which customers were upset last week?'
  • Terminology:
    • MCP
    • live lookup
    • vectorization
    • API sync
  • Why it matters: Highlights why a syncing layer and local cache/vector database are required alongside live tools.

Different Tiers of Context

  • Explanation: Categorizes context into four distinct tiers: Prompt, skills, and memory; Live API calls; Cached data; and Derived context.
  • Key claims:
    • Live API calls are cheaper and easier to build for simple lookups.
    • Cached data is essential for complex semantic queries but requires syncing and vectorization.
    • Derived context involves precomputed summaries and embeddings for faster retrieval.
  • Examples:
    • Account Summary cache refreshed every 15 minutes vs live CRM calls.
  • Terminology:
    • prompt skills and memory
    • live API calls
    • cached data
    • derived context
  • Why it matters: Helps architects choose the right storage and retrieval strategy for different types of agent queries.

Context Selection Strategy and Flow

  • Explanation: Explains the decision tree for handling user prompts: checking skills/memory first, then cached data freshness, falling back to live APIs when necessary.
  • Key claims:
    • Agents should check skills and memory before querying live or cached layers.
    • Caching data reduces timeout risks during large dataset lookups.
  • Examples:
    • Checking skills for customer happiness policies before querying survey data.
  • Terminology:
    • skill match
    • cache freshness
    • live API fallback
    • router
    • summarizer
  • Why it matters: Ensures agents efficiently route queries to the most appropriate context source.

Traceability and Provenance

  • Explanation: Emphasizes the importance of knowing where data came from, who requested it, when it was fetched, and what rules invalidate it for auditing and debugging.
  • Key claims:
    • Every context node passed to a model must be traceable.
    • Provenance is critical for business-critical decisions and versioning.
  • Examples:
    • Tracing a summary back to source ticket IDs and fetch timestamps.
  • Terminology:
    • traceability
    • provenance
    • metadata
    • invalidation
    • versioning
  • Why it matters: Prevents blind reliance on stale or unverified data in automated enterprise decisions.

Key Points

Tools are not enough for context

  • Explanation: Connecting an agent to MCP servers or APIs is insufficient for complex enterprise reasoning; organizations need a structured context graph.
  • Evidence: Live lookup pagination and timeouts when searching thousands of tickets.
  • Practical implication: Architects must implement a syncing and caching layer alongside live tool calls.

More data is not always better

  • Explanation: Pulling massive amounts of raw context causes context window overload and timeouts. Quality and freshness matter more than volume.
  • Evidence: Pulling 5 tickets instead of full history to avoid API timeouts.
  • Practical implication: Use derived summaries and targeted caching strategies.

Traceability is mandatory for enterprise AI

  • Explanation: Models must provide provenance for every piece of data used in decision-making to enable auditing and debugging.
  • Evidence: Citations and source links in chat interfaces.
  • Practical implication: Store metadata, fetch timestamps, scope permissions, and invalidation rules with every context node.

Frameworks, Models & Processes

Context Tier Architecture

  • How it works: Categorizes enterprise knowledge into layers based on access speed, cost, and complexity.
  • Components:
    • Prompt, skills, and memory
    • Live API calls
    • Cached data
    • Derived context
  • When to use: When designing enterprise AI agent architectures that require both real-time actions and historical reasoning.

Context Selection Flow

  • How it works: A decision tree for routing prompts through skills, memory, cache checks, and live API fallbacks.
  • Components:
    • Query received
    • Skill/memory match check
    • Cache freshness check
    • Live API fallback
    • Summarization and generation
  • When to use: When implementing agentic routing and tool execution logic.

Examples & Case Studies

Asking an agent why customer A was upset last week using a single Zendesk tool call.

  • Illustrates: The sufficiency of live API lookups for simple, scoped queries.
  • Lesson: Live tool calls work well when the data scope is narrow.

Asking an agent which customers were upset over the last year.

  • Illustrates: The failure of live lookups due to pagination limits and timeouts.
  • Lesson: Complex semantic queries require a synced local cache and vector database.

Checking account summary cache freshness before calling the CRM API.

  • Illustrates: Optimizing context selection using tiered storage.
  • Lesson: Use cached data when fresh enough; fall back to live APIs only when necessary.

Actionable Takeaways

  • Immediate:
    • Audit your agent context sources and identify what requires local syncing vs live API calls.
    • Implement metadata tracking for data provenance on all ingested context nodes.
  • Strategic:
    • Build a multi-tiered context strategy combining prompts, memory, cached vector stores, and live tools.
    • Treat context selection and routing as core enterprise business logic.
  • Questions to investigate:
    • How do we handle cache invalidation for rapidly changing enterprise data sources?
    • What is the cost tradeoff between vectorizing enterprise databases versus making live API calls?

Claims Worth Verifying

  • No enterprise platforms incentivize vectorizing data because of cost and maintenance. (industry observation)
  • Live lookup methodologies in platforms like MCP do not support live aggregation across vast datasets. (architectural claim)

Notable Quotes

"Why your company needs a context graph and how to build it" (at 0:30) "Tools aren't context" (at 22:13)

Compressed Summary

  • Context graphs encompass third-party data, structured context, memories, and skills.
  • Context tiers include prompts/skills/memory, live API calls, cached data, and derived context.
  • Traceability and provenance are essential for auditing enterprise AI decisions.
  • Keywords: context graph, ai agents, caching, provenance, mcp
  • Core insight: Enterprise AI requires more than live tool calls; it needs a structured, multi-tiered context graph with syncing, caching, and strict data provenance.

Core insights

7
Failure Modemedium noveltystrong evidence

MCP-style live lookups are structurally incapable of aggregation queries across large datasets; the distinction is between scoped single-entity lookups ("why was customer A upset last week?") which a single tool call can satisfy, and set-level queries ("which customers were upset last week?") which require a synced, vectorized local cache because live pagination/timeouts make them infeasible.

Why it matters

It defines the exact boundary where tool-calling architectures break and a persistence/sync layer becomes mandatory, rather than a nice-to-have. Engineers wiring agents to SaaS APIs via MCP will hit this on any fleet-wide or historical query.

Generalization

Any read-only tool-call abstraction over a paginated external API fails on aggregate or cross-entity queries; a mirrored, indexed store is required for the set-level query class.

Live lookup methodology in platforms like MCP does not support live aggregation across vast datasets.
Open source video
Asking 'Why was customer A upset last week?' vs 'Which customers were upset last week?'
Open source video
Simple MCP server connections are not enough for complex semantic queries across large company data sets.
Open source video
Architecturemedium noveltystrong evidence

Context is not one store but four tiers with different cost/latency/complexity profiles: (1) prompt/skills/memory, (2) live API calls, (3) cached data, (4) derived context (precomputed summaries and embeddings). Selection is a routing decision through these tiers rather than a single retrieval step.

Why it matters

It reframes 'context management' as a tiered routing policy, changing where caching, summarization, and tool-call responsibility live in the system. Designers choosing one retrieval strategy for all query types will overpay in latency or correctness.

Generalization

Retrieval and context assembly should be modeled as a policy over heterogeneous storage tiers ordered by freshness, cost, and semantic capability, not a single vector DB or tool loop.

Categorizes context into four distinct tiers: Prompt, skills, and memory; Live API calls; Cached data; and Derived context.
Open source video
Live API calls are cheaper and easier to build for simple lookups.
Open source video
Cached data is essential for complex semantic queries but requires syncing and vectorization.
Open source video
Practicemedium noveltystrong evidence

Every context node handed to the model must carry provenance metadata: source, requester, fetch timestamp, scope permissions, and invalidation rules, so that derived artifacts (e.g., a summary) can be traced back to source ticket IDs and timestamps.

Why it matters

Provenance turns an opaque context blob into an auditable, versionable artifact, enabling debugging, invalidation of stale data, and defensible automated decisions. Without it, enterprises cannot safely let agents act on summarized or cached data.

Generalization

Context should be treated as versioned data with lineage metadata, not as transient prompt text, so downstream decisions remain auditable and invalidatable.

Every context node passed to a model must be traceable.
Open source video
Tracing a summary back to source ticket IDs and fetch timestamps.
Open source video
Store metadata, fetch timestamps, scope permissions, and invalidation rules with every context node.
Open source video
Mental Modelhigh noveltymoderate evidence

Vendors/platforms are structurally disincentivized from vectorizing or pre-indexing customer data because of ongoing cost and maintenance, so this responsibility falls to the enterprise building the agent.

Why it matters

It predicts that the context/sync layer will remain a first-party engineering burden rather than something inherited from SaaS connectors, informing build-vs-buy and team-scoping decisions.

Generalization

Infrastructure providers optimize for the cheapest integration path (pass-through API access), pushing durable indexing and sync costs onto the consumer of the data.

Platforms do not incentivize vectorizing data due to cost and maintenance.
Open source video
Mechanismmedium noveltymoderate evidence

The context selection decision tree runs skills/memory match first, then cache freshness check, then live API fallback, then summarization/generation—ordering tiers by cheapness and stability before reaching to external systems.

Why it matters

Gives a concrete, implementable routing order that avoids unnecessary external calls and timeouts while letting stable procedural knowledge (skills) and memory precede volatile data fetches.

Generalization

Agent retrieval should be ordered by increasing cost and volatility, short-circuiting on cheap, stable sources before escalating to expensive external calls.

Agents should check skills and memory before querying live or cached layers.
Open source video
A decision tree for routing prompts through skills, memory, cache checks, and live API fallbacks.
Open source video
Caching data reduces timeout risks during large dataset lookups.
Open source video
Mental Modelmedium noveltymoderate evidence

A 'context graph' is a conceptual framing of the company's shared brain—third-party data + structured context + memories + skills—not a literal node-and-edge graph database, and skills are treated as a first-class component of context alongside data.

Why it matters

Prevents architects from prematurely adopting graph-DB infrastructure and elevates procedural skills to context, which changes how memory and tooling layers are scoped.

Generalization

'Graph' in agent context design often denotes an organized knowledge model, not a specific storage engine; procedural capabilities belong in the same layer as retrieved facts.

A context graph consists of third-party data, structured context, memories, and skills.
Open source video
rather than literal node-and-edge graph databases
Open source video
Skills are a vital part of a context graph.
Open source video
Tradeofflow noveltymoderate evidence

Retrieving more raw context actively degrades reliability: pulling full histories causes context-window overload and API timeouts, so targeted derived summaries (e.g., pulling 5 tickets instead of the full history) are preferred over completeness.

Why it matters

It inverts the intuition that more context equals better answers, giving an operational rule to cap raw payloads and rely on summaries for breadth.

Generalization

Context assembly should optimize for bounded, relevance-filtered payloads; unbounded retrieval trades correctness for latency and failure risk.

Pulling massive amounts of raw context causes context window overload and timeouts. Quality and freshness matter more than volume.
Open source video
Pulling 5 tickets instead of full history to avoid API timeouts.
Open source video

Deep dives

4

Context-tier routing as an explicit, testable policy

Research question

Does a router that orders context sources by cost, freshness, and volatility (skills/memory → cache freshness check → live API → summarization) outperform a single-strategy retrieval baseline (vector-only or tool-loop-only) on correctness, p95 latency, and external-call volume across a mixed query workload?

Why

If context management is really a routing policy, then the router becomes a first-class, evaluable component — and teams that keep a single retrieval strategy will systematically overpay in either latency or correctness. Quantifying the cross-over points turns 'context management' from folklore into a tunable engineering surface.

Categorizes context into four distinct tiers: Prompt, skills, and memory; Live API calls; Cached data; and Derived context.
Open source video
Agents should check skills and memory before querying live or cached layers.
Open source video
A decision tree for routing prompts through skills, memory, cache checks, and live API fallbacks.
Open source video
Source video

Where live tool-call lookups structurally break: the aggregate-query boundary

Research question

At what dataset size, page depth, or query cardinality does an MCP-style live lookup measurably exceed an acceptable latency/error budget for set-level questions ("which customers were upset last week?") relative to a synced vectorized cache?

Why

This is the falsifiable boundary that decides whether a sync layer is mandatory or optional for a given enterprise. A concrete threshold (records, pages, p95 seconds) converts an architectural argument into capacity planning and a build-vs-skip decision per data source.

Live lookup methodology in platforms like MCP does not support live aggregation across vast datasets.
Open source video
Simple MCP server connections are not enough for complex semantic queries across large company data sets.
Open source video
Asking 'Why was customer A upset last week?' vs 'Which customers were upset last week?'
Open source video
Source video

Minimal viable provenance schema and its overhead budget

Research question

What is the smallest provenance schema (source, requester, fetch timestamp, scope permissions, invalidation rules) that still supports tracing a derived summary back to source record IDs, and what is its measured storage and token overhead per context node?

Why

Provenance is the precondition for letting agents act on cached or summarized data, but unbounded metadata bloats both context windows and storage. Finding the knee of the utility-vs-overhead curve determines whether provenance ships in v1 or gets deferred — and deferring it makes stale data invalidatable in theory only.

Every context node passed to a model must be traceable.
Open source video
Store metadata, fetch timestamps, scope permissions, and invalidation rules with every context node.
Open source video
Tracing a summary back to source ticket IDs and fetch timestamps.
Open source video
Source video

Deriving per-context-type freshness policies from staleness-induced error cost

Research question

Can freshness thresholds be set per context type by empirically measuring the error rate induced by staleness against refresh cost (for 15-minute account summaries versus long-lived documents), rather than by picking a fixed cadence?

Why

Every cached tier collapses into a staleness problem the moment it is written, and the only defensible threshold is one derived from the cost of acting on stale data. Without this, freshness is guessed, and invalidation rules have no calibration target.

Cached data is essential for complex semantic queries but requires syncing and vectorization.
Open source video
Caching data reduces timeout risks during large dataset lookups.
Open source video
Derived context involves precomputed summaries and embeddings for faster retrieval.
Open source video
Source video

Article ideas

3

MCP Is a Lookup Mechanism, Not a Context Layer

Tool-call access over SaaS APIs solves the single-entity question and structurally fails the set-level question; the aggregate query class is the dividing line where a first-party sync-and-index layer stops being optional and becomes core infrastructure. Teams that treat MCP as their retrieval architecture will discover this only on their first fleet-wide or historical query.

Angle

Draw the boundary precisely — one-entity lookups versus aggregate/cross-entity queries — and show what each architecture can and cannot answer, then argue that the sync layer carries its own freshness, cost, and failure surface that must be funded as infrastructure.

Source video

Stop Calling It Retrieval: Context Assembly Is a Routing Policy

Context is not one store but four tiers with distinct cost, freshness, and semantic-capability profiles; the engineering work is dispatch across those tiers (skills/memory first, cache with a freshness check, live APIs as fallback, summaries for breadth), not selecting a single vector database. Committing to one retrieval strategy for all query types guarantees you overpay in latency or correctness.

Angle

Reframe context management as an ordered decision tree with short-circuits, and treat the router as a component with its own evaluation criteria rather than an implicit code path.

Source video

Your Vendor Will Never Index Your Data, So Build the Sync Layer

Platform providers are structurally steered toward the cheapest integration path — pass-through API access — while vectorization and pre-indexing carry ongoing cost and maintenance they have no incentive to absorb. The durable indexing burden therefore lands on the enterprise consuming the data, which should shape build-vs-buy and team-scoping decisions before the first connector is chosen.

Angle

Argue from incentive structure rather than capability: predict where vendor roadmaps will and will not go, and what that means for owning the ingestion, embedding, and invalidation pipeline.

Source video

Project ideas

3

TierRouter: cost/freshness-aware context dispatcher benchmark

beyond-evals

A router that short-circuits on skills/memory, then cache with a freshness check, then live API fallback, reduces external API calls and timeout-driven failures versus a vector-only or tool-loop-only baseline without degrading answer correctness on a mixed query workload.

Proof of concept

Implement a router over four stubbed tiers (skills/memory, live API, cache, derived summaries) with a small labeled query mix containing both single-entity and set-level questions; instrument tier hit rates, p95 latency, external call counts, and timeout failures; A/B against two single-strategy baselines.

Measurement

External call volume per query, p95 end-to-end latency, timeout rate, and answer-correctness rate against the labeled set; report the crossover point where caching beats live lookup.

Source video

ContextLineage: minimal provenance schema with invalidation

gatehouse

Attaching a minimal provenance record (source, requester, fetch timestamp, scope permissions, invalidation rules) to every context node costs under a small fixed overhead in storage and tokens while enabling automated invalidation that measurably reduces staleness-induced wrong answers.

Proof of concept

Store context nodes with lineage metadata, generate a derived summary, and implement a trace path from summary back to source record IDs plus a rule engine that invalidates derived artifacts when sources change or permissions are revoked.

Measurement

Per-node storage and token overhead; percentage of derived summaries successfully traced to source IDs; reduction in staleness-induced errors after invalidation versus a no-provenance control.

Source video

LiveLookupFence: probing the aggregate-query failure boundary

new

For a major SaaS-style API, a live paginated lookup exceeds a practical p95 latency and timeout budget once a query becomes cross-entity or historical beyond a determinable page depth, while a synced index answers the same query within budget.

Proof of concept

Generate synthetic tenant data at increasing scale, run identical single-entity and set-level questions through a live paginated lookup path and a synced vectorized cache, and chart latency, pagination depth, and failure rate against dataset size.

Measurement

Page depth and dataset size at which live lookup p95 latency crosses the threshold and timeout failures appear; accuracy of cached answers versus live ground truth at each scale.

Source video

Architectural implications

4

Live tool calls cannot answer set-level/aggregate questions across large datasets, forcing a separate indexing layer.

Before

Agent connects to MCP servers/APIs and queries them live for any question.

After

Agent routes to a synced, vectorized cache for aggregate/semantic queries, using live APIs only for scoped lookups.

Consequence

A sync/ingestion pipeline becomes core infrastructure with its own freshness, cost, and failure surface.

Source video

Provenance metadata must accompany every context node passed to the model.

Before

Context is assembled as opaque prompt text with no lineage.

After

Each node carries source, requester, timestamp, permissions, and invalidation rules.

Consequence

Context schemas, storage, and invalidation logic must be designed up front; decisions become auditable and stale data invalidatable.

Source video

Context selection is a tiered routing problem, not a single retrieval step.

Before

One retrieval strategy (vector DB or live tool loop) handles all queries.

After

A router/summarizer dispatches across skills/memory, cache (with freshness check), and live APIs.

Consequence

Routing policy becomes an explicit, testable component with its own evaluation criteria.

Source video

Skills are part of the context graph, alongside data and memory.

Before

Skills/tools are treated as separate from retrieved context.

After

Skills are a first-class context tier consulted before data fetches.

Consequence

Memory and capability layers may be co-located and versioned together.

Source video

Tradeoffs and failure modes

4

Live API lookups vs cached/derived context

Benefit

Live API calls are cheaper and easier to build for simple, scoped lookups and always reflect current state.

Cost or risk

They cannot perform live aggregation across vast datasets and fail via pagination/timeouts on set-level queries.

Live lookup methodology in platforms like MCP does not support live aggregation across vast datasets.
Open source video
Source video

Cached and vectorized data

Benefit

Enables complex semantic queries and avoids timeout risk on large dataset lookups.

Cost or risk

Requires ongoing syncing and vectorization, which platforms avoid due to cost and maintenance; cache can go stale without invalidation rules.

Cached data is essential for complex semantic queries but requires syncing and vectorization.
Open source video
Source video

Raw context volume

Benefit

More raw context could improve recall and completeness.

Cost or risk

Overloads the context window and triggers API timeouts, degrading reliability.

Pulling massive amounts of raw context causes context window overload and timeouts. Quality and freshness matter more than volume.
Open source video
Source video

Derived summaries (precomputed)

Benefit

Faster retrieval and bounded payloads for broad queries.

Cost or risk

Introduces staleness and requires provenance to trace back to sources; summaries can drift from underlying truth.

Derived context involves precomputed summaries and embeddings for faster retrieval.
Open source video
Source video

Open questions

4

How should cache freshness thresholds be chosen per context type (e.g., 15-minute account summaries vs longer-lived documents)?

Why unresolved

The summary cites an example refresh cadence but no principle for setting or adapting it.

Research direction

Empirically measure staleness-induced errors vs refresh cost across query classes to derive freshness policies.

Source video

What is the minimal provenance schema that remains useful without bloating context or storage?

Why unresolved

Provenance fields are enumerated but their cost and retrieval implications are not quantified.

Research direction

Prototype provenance schemas and measure audit/debug utility against storage and latency overhead.

Source video

How should the router decide between cached/derived context and live APIs when freshness and cost conflict?

Why unresolved

A decision order is given but not a method for resolving conflicting freshness/cost signals.

Research direction

Build a routing policy and benchmark correctness, latency, and cost across query mixes.

Source video

Does vectorizing/syncing third-party data truly remain uneconomical for platforms, or is this a temporary configuration?

Why unresolved

The claim about platform incentives is asserted without evidence or counterexamples.

Research direction

Compare total cost of ownership of platform-provided indexing vs first-party sync pipelines over time.

Source video

Key claims

8
causalVerification needed

Simple MCP server connections are insufficient for complex semantic queries across large company datasets.

Evidence

Simple MCP server connections are not enough for complex semantic queries across large company data sets.

Question

At what data scale and query type does a live MCP lookup measurably fail versus a synced cache?

Source video
factualVerification needed

Live lookup methodology in MCP-style platforms cannot perform live aggregation across vast datasets.

Evidence

Live lookup methodology in platforms like MCP does not support live aggregation across vast datasets.

Question

What are the pagination/timeout limits of major SaaS APIs that make live aggregation infeasible?

Source video
opinionVerification needed

Platforms do not incentivize vectorizing data because of cost and maintenance.

Evidence

Platforms do not incentivize vectorizing data due to cost and maintenance.

Question

Do platform roadmaps or pricing show deliberate avoidance of hosted indexing/vectorization?

Source video
factualVerification not requested

A context graph consists of third-party data, structured context, memories, and skills, not a literal node-and-edge graph database.

Evidence

A context graph consists of third-party data, structured context, memories, and skills.

Source video
causalVerification needed

Agents should check skills and memory before querying live or cached layers.

Evidence

Agents should check skills and memory before querying live or cached layers.

Question

Does prioritizing skills/memory first reduce latency and external call volume without hurting answer quality?

Source video
opinionVerification not requested

Every context node passed to a model must be traceable.

Evidence

Every context node passed to a model must be traceable.

Source video
causalVerification needed

Pulling large amounts of raw context causes context window overload and timeouts, so quality and freshness matter more than volume.

Evidence

Pulling massive amounts of raw context causes context window overload and timeouts. Quality and freshness matter more than volume.

Question

How does answer quality vary with raw-context volume versus derived-summary volume at fixed token budgets?

Source video
comparativeVerification needed

Cached data is essential for complex semantic queries but requires syncing and vectorization.

Evidence

Cached data is essential for complex semantic queries but requires syncing and vectorization.

Question

What sync latency and index quality are required before cached answers match live data correctness?

Source video

Connections

5