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Evidence-Based Analysis

12 hypotheses, pressure-tested.

We took testable claims from the conference and checked them against the full transcript corpus.

Evidence scorecard
Supported6
Partially supported6
Insufficient evidence0
Contradicted0
H1

MCP Will Become the Default Agent-Tool Interface by 2027

Partially supported

The Model Context Protocol will displace bespoke tool-calling integrations and become the dominant standard through which agents connect to external systems, displacing both raw API calls and competing protocols within 24 months.

Confidence: medium

H2

Context Engineering Has Replaced Prompt Engineering as the Core Skill of AI Engineers

Supported

Prompt engineering — crafting the wording of individual instructions — has been superseded by context engineering: the discipline of deciding what information to place in (and keep out of) an agent's context window, including memory management, skill design, and tool stack curation.

Confidence: high

H3

Small Teams with AI Agents Will Outperform Large Engineering Orgs Within 24 Months

Partially supported

Teams of 2-20 people using AI agents will consistently deliver more output than traditional engineering teams 5-10x their size, creating structural pressure on large engineering organizations that fail to adapt.

Confidence: medium

H4

Agentic Systems Are Creating Technical Debt Faster Than Teams Can Review It

Supported

AI coding agents are producing code complexity, security vulnerabilities, and architectural entropy at a rate that exceeds the capacity of engineering teams to review, understand, and maintain — with measurable consequences for codebase health within months of agent adoption.

Confidence: high

H5

On-Device AI Models Will Handle 30%+ of Consumer Agent Workloads Within 18 Months

Partially supported

Advances in edge model efficiency — particularly Google's Gemma 4 E2B/E4B, Apple Silicon NPU utilization, and LiteRT — will enable consumer devices to handle a substantial fraction of agent tasks locally, reducing cloud dependency and enabling always-on personal agents without per-query API costs.

Confidence: medium

H6

Traditional CI/CD Pipelines Will Be Replaced by Continuous Compute Architectures Within 3 Years

Partially supported

The batch-oriented, human-pace CI/CD model — designed around one to two engineer commits per week — cannot sustain thousands of daily agent PRs and will be replaced by stateful continuous compute environments where validation is inline to the agent loop, not a post-hoc gate.

Confidence: medium

H7

LLM-as-Judge Evaluation Will Become Unreliable at Scale Without Continuous Human Calibration

Partially supported

As AI agents generate outputs faster than humans can review them, LLM-based evaluators will drift from human intent without ongoing annotation campaigns — creating a false confidence problem that will cause silent quality degradation in production agent systems.

Confidence: medium

H8

Code Mode (LLM-Generated Executable Code) Will Displace JSON Tool Calling for Complex Agentic Tasks

Supported

Rather than declaring tool schemas as JSON and invoking them one at a time, LLMs will increasingly generate executable code (JavaScript, Python) that composes multiple API calls, applies logic, and runs in sandboxed runtimes — reducing token cost by 70-99% and eliminating round-trip latency for comp...

Confidence: medium

H9

The 'Chat-First' Agent Interface Paradigm Is Already Obsolete for Professional Use Cases

Supported

Chat-based interfaces are fundamentally inadequate for professional agentic workflows — they are one-dimensional, low-bandwidth, and collapse tree-structured work processes into linear threads — and will be replaced by domain-specific high-bandwidth artifacts (annotatable documents, tabular reviews,...

Confidence: medium

H10

Graph-Augmented RAG Will Become the Standard for Enterprise Agent Knowledge Retrieval

Partially supported

Vector-only RAG systems will be superseded by graph-augmented retrieval architectures that combine vector embeddings with knowledge graph traversal, enabling multi-hop reasoning, decision provenance, and explainability that pure vector search cannot provide — particularly in regulated industries.

Confidence: medium

H11

AI Inference Speed Has Reached the 'Memory Wall' — Hardware Architecture, Not Model Capability, Is the Binding Constraint

Supported

The primary bottleneck limiting AI agent throughput is no longer model capability or parameter count, but memory bandwidth: current GPU architectures must move weights from off-chip HBM to on-chip compute on every token, creating a memory wall that commodity scaling cannot solve without architectura...

Confidence: high

H12

Governance, Not Capability, Is the Binding Constraint on Enterprise AI Agent Deployment

Supported

The primary reason most enterprise AI agent projects stall between proof-of-concept and production is not model capability, tooling immaturity, or cost — it is the absence of governance infrastructure: approval workflows, access control, audit trails, and organizational approval processes built for ...

Confidence: high