AI Engineer · Published 2026-10-10

AI Agents Don't Read Your Policy Docs. They Hit Your APIs — Gravitee

privateReview: unreviewedConfidence: high
AI agentsapi gatewaygovernancesecuritysemantic cachingagent-governanceapi-gatewayprompt-injectionMCPsemantic-cachingrate-limitingshadow-aicost-control
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Summary

Overview

  • Speaker: Sam
  • Channel: AI Engineer
  • Main topic: AI Agent Governance, API Gateways, and Security Architecture
  • Purpose: To demonstrate why AI agent governance must move from agent-level prompts and policies to gateway-level infrastructure enforcement for improved security, scalability, and cost control. The speaker, Sam, CTO at Gravitee, argues that relying on user prompts, markdown files, or agent-level permissions to enforce AI policy and security does not scale and fails in production. Instead, AI governance, identity management, cost control, semantic caching, and rate limiting must be shifted upstream to the infrastructure layer using an API gateway. Through live architectural comparisons, Sam demonstrates how gateway-level control blocks unauthorized API calls, prevents PII leaks, and manages LLM costs efficiently.

Topic Map

The State of AI Agent Adoption and Governance Deficit

  • Explanation: Discusses recent survey results showing 80% of organizations have AI agents in production, but only 14% have any governance around them, leading to severe risks like database loss and PII exposure.
  • Key claims:
    • 80% of organizations have AI agents in production.
    • Only 14% of organizations have governance around AI agents.
    • AI agents inherently want to please users and fulfill requests, often leading to policy violations if left ungoverned.
  • Examples:
    • LLMs bypassing policy documents at runtime because instructions in markdown or prompts are ignored by the agent during execution.
  • Terminology:
    • AI Policy
    • Agent Governance
    • Prompt Injection
  • Why it matters: Organizations face heavy fines and severe data leaks if AI agents have unconstrained access to backend tools and databases.

Shifting Governance from Prompts to Gateway Infrastructure

  • Explanation: Explains the core thesis: remove security and governance constraints from the agent prompt or markdown files and enforce them at the API gateway layer.
  • Key claims:
    • AI agents want to please users and will execute tools if they have access.
    • Runtime enforcement must happen at the gateway level rather than relying on LLM self-regulation.
    • Admin permissions and OAuth/JWT claims must be validated at runtime specifically for agent tasks.
  • Examples:
    • An agent attempting to delete all hotel bookings via an MCP tool, which succeeds when governed only by prompt instructions, but is blocked with a 403 error when governed by a gateway.
  • Terminology:
    • Gravitee Gateway
    • MCP Tool
    • Gateway-level Governance
    • JWT Claims
  • Why it matters: Ensures deterministic security controls that cannot be subverted by prompt injection or model behavior.

Data Exfiltration Protection and PII Leak Prevention

  • Explanation: Demonstrates how an AI agent asked to dump guest lists for compliance is blocked at the gateway when lacking appropriate permissions, preventing PII leaks.
  • Key claims:
    • LLMs trying to be helpful will attempt to retrieve and return sensitive guest data if tools are available.
    • Gateway inspection stops PII leaks before data leaves the infrastructure perimeter.
  • Examples:
    • An agent requesting full guest details (emails, card numbers) being blocked by gateway-level permissions.
  • Terminology:
    • Data Exfiltration
    • PII Leak
    • MCP Discovery
    • OpenTelemetry
  • Why it matters: Protects organizations from severe GDPR, HIPAA, and regulatory compliance breaches.

Token Spend and Cost Control via Gateway Rate Limiting

  • Explanation: Explores how rate limiting and token spend controls at the gateway level prevent runaway LLM costs from repetitive tool calls and buggy loops.
  • Key claims:
    • Repetitive tool calls and buggy agent loops can generate massive token and API costs.
    • Gateway rate limiting restricts requests per minute without burdening the downstream LLM or MCP server.
  • Examples:
    • A buggy agent making 8 identical requests for hotel bookings in a loop, rate-limited by the gateway after 3 requests.
  • Terminology:
    • Rate Limiting
    • Token Spend
    • LLM Cost Optimization
  • Why it matters: Prevents unexpected financial losses from runaway agentic workflows and recursive API calls.

Semantic Caching for Zero Latency and Zero Cost

  • Explanation: Demonstrates semantic caching using Redis at the gateway layer to serve similar user prompts instantly without making downstream LLM calls.
  • Key claims:
    • Similar user prompts (e.g. IT helpdesk queries) can be cached semantically at the gateway.
    • Semantic caching provides zero cost and zero latency for repeated intents.
  • Examples:
    • A user asking 'List all my hotel bookings' 5 times being served from the Redis cache after the first call, reducing token spend significantly.
  • Terminology:
    • Semantic Caching
    • Redis
    • Cache Hit
    • Latency Reduction
  • Why it matters: Scales customer support and internal AI tools efficiently while cutting operational costs to zero for frequent queries.

Edge Management and Shadow AI Control

  • Explanation: Addresses shadow AI traffic where employees use local laptops and unmonitored AI tools, proposing edge management and gateway enforcement to secure all shadow AI traffic.
  • Key claims:
    • Employees using local laptops to run AI tools create massive shadow AI blind spots.
    • Edge management capabilities in gateways provide visibility, logging, observability, and forced routing for shadow AI traffic.
  • Examples:
    • Enforcing shadow AI traffic through a gateway to maintain infosec compliance in heavily regulated industries.
  • Terminology:
    • Shadow AI
    • Edge Management
    • Infosec Compliance
    • Blast Radius
  • Why it matters: Enterprise security teams must maintain visibility and control over all AI traffic across the organization.

Key Points

Prompts are not security boundaries

  • Explanation: Relying on prompt instructions or markdown policies to restrict AI agent behavior fails because LLMs prioritize user helpfulness and can be jailbroken or bypassed.
  • Evidence: Demonstrated live where prompt-governed agents executed unauthorized deletions, while gateway-governed agents were blocked.
  • Practical implication: Security and governance must be enforced in infrastructure, not in natural language prompts.

Decoupling front-end agents from back-end services

  • Explanation: Agents should be decoupled from MCP servers, databases, and LLMs using an API gateway to manage permissions, routing, and caching.
  • Evidence: Gateway intercepting tool calls and evaluating JWT claims and runtime permissions.
  • Practical implication: Enables scalable multi-agent architectures without exploding infrastructure complexity.

Gateway-level cost and rate controls

  • Explanation: Enforcing token limits and rate limits at the gateway prevents runaway loops from draining budgets.
  • Evidence: Comparison of ungoverned multi-call loops versus gateway rate-limited calls.
  • Practical implication: Protects organizations from unpredictable financial exposure in production AI systems.

Frameworks, Models & Processes

Gravitee AI Gateway Architecture

  • How it works: Sits between front-end AI agents/LLMs and back-end tools/MCP servers/databases, inspecting traffic, evaluating permissions, applying semantic caching, and enforcing rate limits.
  • Components:
    • Gravitee Gateway
    • Data Bricks / Backend databases
    • Sonic / Groq for inference
    • Semantic Cache (Redis)
  • When to use: When deploying AI agents to production that require enterprise-grade security, cost control, and governance.

Examples & Case Studies

An AI agent instructed via prompt not to delete bookings was asked by a user to delete all bookings and successfully executed the deletion tool.

  • Illustrates: The failure of prompt-based policy enforcement.
  • Lesson: Never trust LLM compliance with security policies via natural language instructions.

An agent with gateway-level permission enforcement attempted the same deletion task and received a 403 Forbidden error because runtime permissions were missing.

  • Illustrates: Successful gateway-level security enforcement.
  • Lesson: Enforce authorization at the gateway infrastructure level.

Actionable Takeaways

  • Immediate:
    • Audit existing AI agents for prompt-only security controls.
    • Evaluate API gateway solutions for AI traffic management.
  • Strategic:
    • Move governance and compliance from application layer prompts to infrastructure gateways.
    • Implement semantic caching and rate limiting to control LLM operational costs at scale.
  • Questions to investigate:
    • How do we handle shadow AI traffic across employee laptops?
    • What is the performance overhead of semantic caching at the gateway layer?

Claims Worth Verifying

  • 80% of organizations had an agent in production in recent surveys, but only 14% had governance around it. (statistical)
  • Semantic caching reduces repeated query costs and latency to zero. (architectural)

Notable Quotes

"Rather than relying on a user prompt to get it correctly or some markdown files kind of limiting what it can do, like remove that out the equation, remove it to the gateway, remove it to like the governance layer." "AI's really helpful, like it wants to please, it wants to give you what you want."

Compressed Summary

  • Prompt-based AI governance fails in production because LLMs prioritize user helpfulness.
  • API gateways provide deterministic security, rate limiting, and access control for AI agents.
  • Semantic caching at the gateway drastically reduces LLM costs and latency.
  • Edge management secures shadow AI traffic from unmonitored user devices.
  • Keywords: ai agents, api gateway, governance, security, semantic caching
  • Core insight: AI agent security and governance cannot rely on prompt instructions; they must be enforced upstream via API gateway infrastructure.

Core insights

6
Architecturemedium noveltystrong evidence

Governance of agent behavior cannot be expressed as natural-language prompts or markdown policies because the agent bypasses them at runtime; it must be enforced deterministically at the API gateway layer that sits between the agent and the tools/backends it calls.

Why it matters

This relocates the trust boundary for agent safety from the model's instruction-following to infrastructure that is immune to prompt injection and model behavior drift, making security guarantees deterministic rather than probabilistic.

Generalization

Any system where an autonomous component decides which privileged backend operations to invoke should enforce authorization on the invocation path, not in the component's instructions — the same reason you don't rely on client-side validation.

the agent during execution
Open source video
Security and governance must be enforced in infrastructure, not in natural language prompts.
Open source video
Mechanismmedium noveltymoderate evidence

Agent identity and task-scoped authorization should be validated per-tool-call at the gateway using OAuth/JWT claims evaluated specifically for the agent's task context, rather than trusting the agent's own scoping.

Why it matters

Runtime claim evaluation lets the same agent hold broad model capability while each downstream call is independently authorized, preventing privilege escalation on individual tool invocations.

Generalization

Per-request authorization scoped to the calling workload's identity is a reusable pattern for any tool-using agent that touches sensitive APIs.

Admin permissions and OAuth/JWT claims must be validated at runtime specifically for agent tasks.
Open source video
Gateway intercepting tool calls and evaluating JWT claims and runtime permissions.
Open source video
Mechanismmedium noveltymoderate evidence

Semantic caching (Redis) at the gateway can serve similar user intents — not just exact-match requests — without a downstream LLM call, reducing token spend to zero and latency to near-zero for repeated intents.

Why it matters

Caching on semantic intent rather than exact payload is a cost/latency lever placed outside the agent, so it benefits every agent hitting the gateway without per-agent code changes.

Generalization

Intent-level caching is applicable to any high-volume AI endpoint with a skewed query distribution (helpdesk, internal search).

Similar user prompts (e.g. IT helpdesk queries) can be cached semantically at the gateway.
Open source video
A user asking 'List all my hotel bookings' 5 times being served from the Redis cache after the first call, reducing token spend significantly.
Open source video
Practicemedium noveltymoderate evidence

Runaway cost from buggy agent loops (e.g. 8 identical tool calls) is best contained by gateway rate limiting per minute rather than by the LLM or downstream MCP server, keeping the burden off the model and backend services.

Why it matters

Places a deterministic cost circuit breaker on the traffic path that operates regardless of whether the agent's loop-detection logic works.

Generalization

Rate-limiting the tool-call surface, not the model call, is the right chokepoint for agent cost control since one agent turn can fan out into many backend calls.

A buggy agent making 8 identical requests for hotel bookings in a loop, rate-limited by the gateway after 3 requests.
Open source video
Gateway rate limiting restricts requests per minute without burdening the downstream LLM or MCP server.
Open source video
Predictionmedium noveltyweak evidence

Shadow AI traffic from employees running tools on local laptops is only governable by forcing that traffic through a gateway/edge-management layer for visibility, logging, and forced routing.

Why it matters

Extends the governance boundary beyond first-party agents to all AI traffic an organization generates, which is necessary for regulated industries.

Generalization

As AI usage becomes ubiquitous and decentralized, infrastructure-level interception rather than per-app instrumentation becomes the only viable observability strategy.

Employees using local laptops to run AI tools create massive shadow AI blind spots.
Open source video
Edge management capabilities in gateways provide visibility, logging, observability, and forced routing for shadow AI traffic.
Open source video
Architecturemedium noveltymoderate evidence

Agents should be decoupled from MCP servers, databases, and LLMs by an API gateway that owns permissions, routing, and caching, so multi-agent architectures can scale without the agent itself embedding backend integration logic.

Why it matters

Centralizes cross-cutting concerns (auth, routing, cache, rate limits) in one place and prevents N agents from each reimplementing and mis-implementing backend coupling.

Generalization

This is the classic service-mesh/sidecar argument applied to agent-to-tool traffic, suggesting agent backends will converge on an infrastructure-mediated control plane.

Agents should be decoupled from MCP servers, databases, and LLMs using an API gateway to manage permissions, routing, and caching.
Open source video
Enables scalable multi-agent architectures without exploding infrastructure complexity.
Open source video

Deep dives

4

Falsifying prompt-based agent governance: repeatability and boundary conditions of runtime policy bypass

Research question

Under what conditions does prompt/markdown-level agent governance reliably fail to block unauthorized tool calls, and what is the bypass rate as a function of prompt-injection pressure, context length, and tool surface size?

Why

The entire case for moving governance to the gateway rests on the empirical claim that prompt-level policy is bypassed at execution time. If bypass is rare or model-version-dependent, the infrastructure argument weakens; if it is pervasive, every agent-with-tools deployment needs an enforcement chokepoint on the invocation path.

LLMs bypassing policy documents at runtime because instructions in markdown or prompts are ignored by the agent during execution.
Open source video
An agent attempting to delete all hotel bookings via an MCP tool, which succeeds when governed only by prompt instructions, but is blocked with a 403 error when governed by a gateway.
Open source video
AI agents want to please users and will execute tools if they have access.
Open source video
Source video

Task-scoped agent identity: deriving and propagating per-tool-call claims for gateway authorization

Research question

How does a gateway derive an agent's task-scoped identity and authorized claim set for each individual tool call, and can a token-exchange flow issue narrower credentials per invocation without breaking multi-step agent plans?

Why

Per-call authorization is the mechanism that makes gateway enforcement more than a coarse allow/deny list. Without a concrete scheme for mapping task intent to scoped credentials, the gateway either over-grants (agent identity reused across all tools) or breaks legitimate multi-step workflows.

Admin permissions and OAuth/JWT claims must be validated at runtime specifically for agent tasks.
Open source video
Gateway intercepting tool calls and evaluating JWT claims and runtime permissions.
Open source video
Runtime enforcement must happen at the gateway level rather than relying on LLM self-regulation.
Open source video
Source video

Semantic cache correctness: false-hit rate, staleness, and invalidation for gateway-level LLM response caching

Research question

On a skewed real-world query distribution, what is the semantic-cache false-hit rate (similar but not equivalent intents), and how much answer correctness degrades versus the cost and latency saved?

Why

The claimed benefit is zero cost and zero latency, but the unaddressed liability is serving semantically adjacent but wrong answers. Quantifying hit precision and invalidation cost determines whether semantic caching is a net win or a correctness hazard at the shared-infrastructure layer.

A user asking 'List all my hotel bookings' 5 times being served from the Redis cache after the first call, reducing token spend significantly.
Open source video
Similar user prompts (e.g. IT helpdesk queries) can be cached semantically at the gateway.
Open source video
Source video

Gateway rate limiting as a deterministic cost circuit breaker for runaway agent loops

Research question

Does per-minute rate limiting on the tool-call surface contain runaway agent loops more effectively than loop detection inside the agent, and at what threshold does it begin to throttle legitimate high-volume agent workloads?

Why

One agent turn can fan out into many backend calls, so the cost chokepoint is the tool-call surface, not the model call. Determining the operating threshold separates a cost circuit breaker from a throughput tax that masks underlying loop bugs.

A buggy agent making 8 identical requests for hotel bookings in a loop, rate-limited by the gateway after 3 requests.
Open source video
Gateway rate limiting restricts requests per minute without burdening the downstream LLM or MCP server.
Open source video
Source video

Article ideas

3

Your Agent's System Prompt Is Not a Security Boundary

Natural-language policy is a suggestion to a component optimized to be helpful; the only governance that survives prompt injection and model drift is enforcement on the invocation path, which means the trust boundary for agents belongs at the gateway, not in the markdown file.

Angle

Treat agent governance as the client-side-validation fallacy of the LLM era: we already know not to trust the caller, yet we keep shipping policy as prose and calling it control.

Source video

Per-Call Authorization for Agents: Why 'Authenticated Agent' Is Not Enough

Authenticating the agent once and granting it a broad token reintroduces the very privilege-escalation risk governance was supposed to remove; agent authorization must be evaluated at each tool invocation against task-scoped claims.

Angle

Borrow the workload-identity and token-exchange patterns from service meshes and apply them to agent-to-tool traffic, with concrete guidance on scoping claims to a task rather than a session.

Source video

The Gateway Is Where Agent Cost Control Actually Happens

Budget controls implemented inside the agent depend on the same loop-detection logic that is failing; semantic caching and per-minute rate limiting on the shared traffic path are the only controls that cap spend regardless of agent correctness.

Angle

Reframe agent FinOps as a network-policy problem and show where the enforcement points sit relative to model calls, MCP servers, and backend databases.

Source video

Project ideas

4

BypassBench: Prompt-Policy Evasion Harness for Tool-Using Agents

gatehouse

A tool-using agent governed only by system-prompt or markdown policy will execute unauthorized mutating tool calls in a measurable fraction of trials, and that fraction is materially reduced (approaching zero) when the same calls are authorized at a gateway using runtime claims.

Proof of concept

Build a pair of identical agent configurations against the same MCP-style tool set: one governed by prompt policy, one behind a gateway that evaluates JWT claims per call. Run a fixed adversarial and benign task suite (including destructive and PII-retrieval tasks) against both and record executed vs. blocked calls.

Measurement

Unauthorized-call execution rate per configuration, blocking latency added by the gateway, and false-block rate on legitimate tasks.

Source video

Task-Scoped Agent Credential Broker

gatehouse

A token-exchange service that mints narrow, short-lived credentials per tool call from a task descriptor can authorize multi-step agent plans without over-granting, keeping the over-grant window bounded by a single invocation.

Proof of concept

Intercept agent tool calls, map each call's intent plus agent identity to a scoped claim set, issue a short-TTL token, and have the gateway validate it. Test on a multi-step plan requiring different privilege levels across steps.

Measurement

Privilege breadth per issued token (count of authorized verbs/resources), plan success rate without manual re-scoping, and number of denied calls caused by scope mismatch.

Source video

Semantic Cache Hit-Precision Audit

beyond-evals

Semantic caching at the gateway reaches high cost savings on skewed query distributions but exhibits a non-trivial false-hit rate on near-miss intents, and the correctness loss is not uniform across query categories.

Proof of concept

Instrument a gateway semantic cache in front of an LLM endpoint, replay a skewed intent distribution (including deliberately near-miss pairs such as variants of 'list my bookings'), and grade cached responses against fresh-model responses.

Measurement

Hit rate, false-hit rate by intent category, answer-equivalence score for cache-served vs. fresh responses, and tokens/latency saved per correct answer.

Source video

Loop-Breaker Threshold Study

new

Per-minute rate limiting at the gateway caps runaway-loop cost with negligible impact on legitimate agent workloads, provided the threshold is set from observed fan-out-per-turn rather than a uniform default.

Proof of concept

Run a deliberately looping agent and a legitimate high-fan-out agent through the same gateway with configurable per-minute limits; sweep thresholds and record cost, completion, and throttling outcomes for each.

Measurement

Tokens/backend calls consumed before and after the cap, legitimate-task completion rate at each threshold, and throttled-request count attributable to true loops vs. valid work.

Source video

Architectural implications

3

The trust boundary for agent safety shifts from the prompt/model to the network edge.

Before

Policy and permission constraints embedded in system prompts, markdown policy files, or agent-level permission config.

After

Constraints enforced at an API gateway that inspects and authorizes each tool call at runtime using JWT/OAuth claims.

Consequence

Security guarantees become deterministic and prompt-injection-resistant, but require the gateway to understand agent identity and task context.

Source video

Tool-call traffic becomes a first-class governed network flow rather than an in-process function call.

Before

Agent directly invokes MCP tools, databases, and LLMs as local integrations.

After

Agent calls route through a gateway that mediates permissions, rate limits, and caching for MCP servers, DBs, and LLMs.

Consequence

Enables shared policy, observability, and cost controls across many agents, at the cost of an added hop and gateway as a new critical dependency.

Source video

Cost and latency controls move from per-agent code into shared infrastructure.

Before

Each agent implements its own retry limits, caching, and budget checks.

After

Gateway provides semantic caching and per-minute rate limiting for all agent traffic.

Consequence

Reduces duplicated logic and enforces uniform budgets, but cache correctness and rate-limit tuning become platform-level concerns.

Source video

Tradeoffs and failure modes

3

Centralizing agent governance in a gateway

Benefit

Deterministic, prompt-injection-resistant enforcement of security, rate limits, and caching across all agents.

Cost or risk

Introduces a gateway as a critical path dependency and single point of enforcement/failure; all agent traffic must be routed through it to be governed.

Security and governance must be enforced in infrastructure, not in natural language prompts.
Open source video
Source video

Semantic caching of LLM responses at the gateway

Benefit

Zero cost and zero latency for repeated or similar intents.

Cost or risk

Serving 'similar' prompts from cache can return responses that are semantically close but wrong for the specific request; correctness risk is not addressed in the summary.

Similar user prompts (e.g. IT helpdesk queries) can be cached semantically at the gateway.
Open source video
Source video

Gateway rate limiting as cost control

Benefit

Stops runaway agent loops from draining budgets without burdening the downstream LLM or MCP server.

Cost or risk

A blanket per-minute limit can throttle legitimate high-volume agent workloads or mask underlying loop bugs rather than fixing them.

A buggy agent making 8 identical requests for hotel bookings in a loop, rate-limited by the gateway after 3 requests.
Open source video
Source video

Open questions

4

How does the gateway determine the agent's task-scoped identity and authorized claim set for each tool call?

Why unresolved

The summary asserts JWT/OAuth claims are validated 'specifically for agent tasks' but does not describe how task scope is derived or propagated.

Research direction

Prototype a token-exchange flow where the agent's task intent maps to a scoped credential issued per tool-call.

Source video

What is the false-hit rate and error profile of semantic caching on agent responses, and how are stale or incorrect cached responses invalidated?

Why unresolved

The summary claims zero cost/latency but gives no accuracy or invalidation mechanism.

Research direction

Benchmark semantic-cache hit precision on a skewed query distribution and measure downstream answer correctness.

Source video

Can gateway-layer enforcement cover all agent egress paths, including shadow AI on local laptops?

Why unresolved

Edge management is claimed to force routing for shadow AI traffic but the enforcement mechanism is not described.

Research direction

Evaluate network-level forced routing and its coverage/evasion surface for unmanaged endpoints.

Source video

What is the actual production incidence behind the 80%-in-production / 14%-governed split, and how was 'governance' defined?

Why unresolved

The statistic is cited from a survey without methodology, sample, or definition of governance.

Research direction

Locate the primary survey and replicate the measure across engineering orgs.

Source video

Key claims

7
factualVerification needed

80% of organizations have AI agents in production.

Evidence

80% of organizations have AI agents in production.

Question

What is the source survey, sample size, and definition of 'in production'?

Source video
factualVerification needed

Only 14% of organizations have governance around AI agents.

Evidence

Only 14% of organizations have governance around AI agents.

Question

How was 'governance' operationalized in the underlying survey?

Source video
causalVerification needed

A prompt-governed agent will execute unauthorized actions (like deleting all hotel bookings) that a gateway-governed agent blocks with a 403.

Evidence

An agent attempting to delete all hotel bookings via an MCP tool, which succeeds when governed only by prompt instructions, but is blocked with a 403 error when governed by a gateway.

Question

Under what conditions does prompt governance reliably fail, and how repeatable is the bypass?

Source video
causalVerification needed

Gateway inspection stops PII leaks before data leaves the infrastructure perimeter.

Evidence

Gateway inspection stops PII leaks before data leaves the infrastructure perimeter.

Question

Does gateway-level inspection catch PII embedded in prompts, tool arguments, and responses across all paths?

Source video
comparativeVerification needed

Gateway rate limiting restricts requests per minute without burdening the downstream LLM or MCP server.

Evidence

Gateway rate limiting restricts requests per minute without burdening the downstream LLM or MCP server.

Question

How much of the load and cost reduction is attributable to gateway vs. upstream controls?

Source video
causalVerification needed

Semantic caching provides zero cost and zero latency for repeated intents.

Evidence

Semantic caching provides zero cost and zero latency for repeated intents.

Question

What is the measured hit rate, latency, and correctness for semantic vs. exact-match caching?

Source video
opinionVerification not requested

Agents should be decoupled from MCP servers, databases, and LLMs using an API gateway to manage permissions, routing, and caching.

Evidence

Agents should be decoupled from MCP servers, databases, and LLMs using an API gateway to manage permissions, routing, and caching.

Source video

Connections

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