Area

Agentic AI

Agentic AI is software that plans a sequence of steps toward a goal, calls tools to carry them out, and adjusts when a step fails. The distinction from a chatbot is control: an agent decides what to do next, rather than answering what it was asked.

Agentic AI

Deloitte

Deloitte built Zora AI, an agentic product platform shipping ready-to-deploy AI agents that perceive, reason, and…

Agentic AI

IBM Consulting

IBM Consulting builds its generative AI practice on watsonx, a platform Gartner named a Leader across…

Agentic AI

Cognizant

Cognizant positions itself as an AI Builder and delivered more than $21 billion in 2025 revenue,…

Agentic AI

PwC

PwC earned a Leader position in The Forrester Wave: AI Consulting Services, Q2 2026, and Forrester's…

Agentic AI

Capgemini

Capgemini reported full-year 2025 revenue of 22.47 billion euros, or roughly $26.65 billion, beating its own…

Agentic AI

Accenture

Accenture built the largest AI practice among the traditional systems integrators, and its financial results back…

Agentic AI

Anthropic

Anthropic built its position by winning the enterprise market that OpenAI built its reputation on. The…

Agentic AI

Palantir

Palantir originated the forward deployed engineering model around 2006, when its data-integration software proved too complex…

Agentic AI

Tredence

Tredence’s generative AI practice centres on building and deploying agentic systems that automate multistep workflows for…

About Agentic AI

Agentic AI is software that plans a sequence of steps toward a goal, calls tools to carry them out, and adjusts when a step fails. The distinction from a chatbot is control: an agent decides what to do next, rather than answering what it was asked.

At a glance
Firms tracked
9
Typical buyer
Operations, IT
Common first use
Ticket triage
Typical pilot length
6 to 12 weeks
Main failure mode
No rollback path
Maturity
Early production

Area reviewed 14 Sep 2026

What the work involves

Four layers, and only one of them is the model

A working agent needs a planner that decomposes a goal, a tool layer with real permissions, a memory of what it already tried, and an evaluation harness that catches a bad run before a human does. Most projects that stall have the first and fourth missing. Ask any vendor which of the four they build and which they assume you already have.

Who buys it

Operations teams with a measurable queue

The buyers who succeed have a repetitive, high-volume process with a clear success metric and a tolerable error cost. Ticket triage, invoice matching, and claims intake all qualify. Anything where a wrong action is expensive and hard to reverse belongs in a later phase, not a pilot.

Firms in this area

Who is doing the work

A

Aulric Labs

Agent platform plus delivery team
London, 2021
Strongest here


T

Tredence

Embedded pods, analytics-led agents
San Jose, 2013
Also covers


H

Halvern Systems

Agent evaluation and guardrails
Berlin, 2022
Specialist


K

Kestrel Works

Workflow automation, agent retrofit
Austin, 2018
Mid-market

See all 19 firms in this area

What to ask

Five questions that separate the real from the packaged

  1. Show me a run that failed. What did the agent do next, and who found out first?
  2. What permissions does it hold in production, and who can revoke them without a deploy?
  3. How do you evaluate a change before it ships, and how many test cases exist today?
  4. What is the rollback path for an action that has already been taken externally?
  5. Which of the four layers do you build, and which am I expected to own?

Read the full explainer

Coverage in this area

Recent reading

16Sep

Blog

How to tell a real agent from a wrapped prompt

16Sep

News

Retailers begin blocking agent traffic

17Sep

Ranking

The firms enterprises shortlist most

G

Glossary

Agent, planner, tool call

Research notes

How this area was assessed

We identified 19 firms claiming agentic delivery, then applied one bar: at least two deployments running in production for more than three months, with a named reference we could speak to. Six cleared it. The most common reason for failing was not capability but evidence: pilots that never reached production, or references the firm would not name.

Stage Firms Why they dropped out
Claimed agentic delivery 19 ·
Had a production deployment 11 8 had pilots only
Two or more, over three months 8 3 had a single deployment
Named reference we could speak to 6 2 declined to name one

What we got wrong last quarter

Our July assessment scored firms partly on which model they used. That was a mistake: the model is the easiest component to swap and told us nothing about delivery capability. The September revision scores the four layers instead, which moved two firms up and one down.

Sources

  1. Firm interviews, 19 firms, June to September 2026
  2. Client reference interviews, 9 completed
  3. Enterprise RFP documents mentioning agent evaluation, 2026
© 2026 Top AI Firms. Independent research \u{2014} we take no payment for placement.How we assess a firm · About ·