Section 01 · The Market

Why agentic operations became viable — and why most attempts still fail.

01Why agents became viable in 2026

Long-horizon reasoning

Models now sustain coherent multi-step objectives across hours of execution, not single turns.

Frontier model capability

Opus 4.x and GPT-5 class models hold complex operational state and recover from partial failure.

Native tool integration

Structured tool calling made deterministic system actions reliable enough for operational use.

Agent runtimes

Persistent execution environments made long-running, resumable agent processes practical.

02The steepest corporate adoption curve since cloud
5%

Enterprise apps embedding task-specific agents, end 2025

40%

Forecast for end 2026

Growth in a single year — faster than cloud or mobile-first adoption

76%

Of enterprise AI use cases are now bought from vendors, not built in-house

03Experimenting is common. Scaling is rare.
62%

of organisations are at least experimenting with AI agents

23%

are scaling agentic systems in at least one business function

<10%

have scaled agents in any single function to deliver real value

The advantage is not being early to experiment — almost everyone is. It is being early to something that actually reached production and stayed there.

04Why in-house AI projects fail
88%

of enterprise AI pilots never reach production

76%

of AI use cases are now bought from vendors, not built in-house

40%+

of agentic AI projects expected cancelled by end of 2027

Built from scratch

In-house teams rebuild orchestration, governance and audit from zero — cost that never amortises.

Experiment, not product

Internal pilots chosen for demo appeal, with no baseline and no route to production.

No harness underneath

Skills and loops without governance or containment — the project stalls at the security review.

Of thousands of vendors marketing agentic AI, under ten in the UK are genuinely agentic and none have Vantage capability in production. The market has moved to buying — Warden and Vantage already exist, in production, with the harness and governance solved.

05Agentic is not the same as skills and an LLM licence
Buyers today — LLMs and skills
  • — ChatGPT / Claude / Copilot licences
  • — Custom skills & connectors (MCP)
  • — RAG and knowledge bases
  • — Prompt chains and one-off scripts
What's missing — agents
  • — Orchestration & supervision
  • — Governance & permissions
  • — Monitoring & audit
  • — Operational control

All of that is AI. None of it is agentic. Skills, connectors and a model licence are components — without Warden's harness they have no orchestration, no governance and no audit trail.

06Agentic operations, already in production
Deutsche Telekom
Telecoms · DE

RAN Guardian identified 237,000 events; major incident handling cut to ~60s.

Vodafone
Telecoms · UK

Agents resolving outages and optimising infrastructure scaling.

Chevron
Energy · US

Agentic AI at the edge assisting autonomous inspection operations.

Shell
Energy · NL/UK

Agentic root-cause analysis monitoring 13,000+ pieces of equipment.

JPMorgan Chase
Banking · US

LLM Suite across ~250,000 employees; agentic AI on complex multistep tasks.

Hawaiian Electric
Utility · US

Regulatory query agent live in two weeks; response time 5 min → 5 sec.

Duke Energy
Utility · US

Self-healing grid at a control centre serving 800,000 customers.

Bell Canada
Telecoms · CA

AIOps prioritising anomalies before escalation; 25% fewer reported issues.

Telefónica
Telecoms · ES

Agents cut 5G network slicing design from weeks to minutes.

Uber
Operations · US

Finch runs ~60,000 agent tasks per week for finance and analytics teams.

Next step

Book a briefing on vantage — in production today.

Thirty minutes with the engineers who built the platform. We show live operations, the audit trail, and the headcount cost line the deployment is benchmarked against.