How enterprises are building AI agents in 2026
New research from 500+ technical leaders reveals how enterprises are deploying AI agents—and why 80% already report measurable ROI.
Athena Intelligence compresses weeks of enterprise knowledge work into hours with Claude
Industry:
Software
Company size:
Small
Product:
AI Platform
Location:
North America
4–8 weeks to production
vs. 6–12 month industry standard
Compresses multi-step analyst workflows
from days to hours
The problem
How enterprises are building AI agents in 2026
New research from 500+ technical leaders reveals how enterprises are deploying AI agents—and why 80% already report measurable ROI.
How enterprises are building AI agents in 2026
New research from 500+ technical leaders reveals how enterprises are deploying AI agents—and why 80% already report measurable ROI.
The solution
Introducing Claude Opus 4.6
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We’re upgrading our smartest model. The new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, and features a 1M token context window.
Introducing Claude Opus 4.6
We’re upgrading our smartest model. The new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, and features a 1M token context window.
Introducing Claude Opus 4.6
We’re upgrading our smartest model. The new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, and features a 1M token context window.
"We needed a model that could reason through ambiguity, follow complex instructions, and explain its reasoning within strict compliance frameworks. Claude was the best fit."
Founder and CEO, Athena Intelligence
The outcome
How enterprises are building AI agents in 2026
New research from 500+ technical leaders reveals how enterprises are deploying AI agents—and why 80% already report measurable ROI.
How enterprises are building AI agents in 2026
New research from 500+ technical leaders reveals how enterprises are deploying AI agents—and why 80% already report measurable ROI.
Video caption
How enterprises are building AI agents in 2026
New research from 500+ technical leaders reveals how enterprises are deploying AI agents—and why 80% already report measurable ROI.
Introducing Claude Opus 4.6
We’re upgrading our smartest model. The new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, and features a 1M token context window.
Introducing Claude Opus 4.6
We’re upgrading our smartest model. The new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, and features a 1M token context window.
Video caption
Introducing Claude Opus 4.6
We’re upgrading our smartest model. The new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, and features a 1M token context window.
Athena Intelligence is a startup that pairs an AI-native workspace with an autonomous agent to handle the repetitive, time-intensive parts of knowledge work. The company serves Fortune 500 enterprises, law firms, financial services organizations, healthcare systems, and government agencies. Claude powers the vast majority of autonomous work on the platform.
With Claude, Athena Intelligence:
- Deploys production enterprise workflows in 4–8 weeks, compared to the 6–12 month industry standard
- Runs 10,000+ autonomous workflows weekly across its customer base
- Over 100k assets (email, pptx, xlsx, docx, etc.) created or edited every month across customers
- Compresses multi-step analyst workflows from days to hours
- Fully automated 400 monthly legal memos at a top-tier law firm, reducing 2–3 days of manual work to an overnight process
- Onboards new departments in hours after initial deployment, using natural language configuration instead of custom model training
The challenge: Knowledge worker overload meets messy enterprise data
Athena's customers across regulated industries share a common set of problems. Analysts spend the bulk of their time on repetitive, low-value tasks rather than strategic analysis. Enterprise data lives across dozens of systems, and synthesizing it requires enormous manual effort. That data is also rarely clean. Poorly documented systems, legacy formats, and conflicting sources are the norm.
Traditional enterprise AI deployments take 6–12 months to go live, and regulated industries demand accuracy, transparency, and auditability at every step. "We needed a model that could reason through ambiguity, follow complex instructions, maintain quality across long workflows, and explain its reasoning within strict compliance frameworks," said Brendon Geils, Founder and CEO of Athena Intelligence. "Claude was the best fit."
Selecting Claude for reasoning and safety
Athena selected Claude for its core intelligence layer based on capabilities that proved decisive in production.
"Claude reliably executes complex, detailed, multi-page instructions where other models drift or generate false information," said Geils. "This is the difference between an AI that demos well and one that operates in production." The Athena team said Claude also maintained consistent quality across workflows with dozens of decision points and hundreds of pages of context, while competitors degraded as complexity increased.
For enterprise environments where data is never perfect, Claude's judgment proved equally important. Geils noted that Claude makes professional-grade decisions about conflicting data sources, incomplete information, and when to ask for clarification, and that it explains its reasoning at every step, which is critical for audit, compliance, and legal work. "Anthropic's commitment to responsible AI aligns with our regulated customers' requirements and builds confidence among compliance teams," Geils said.
The solution: From copilot to autonomous agent
Claude powers the core intelligence layer across every major Athena workflow, and the platform's evolution maps directly to Claude's generational improvements. During the Claude 3 era, Athena functioned primarily as a copilot, useful for individual tasks like querying data and drafting summaries, but multi-step workflows still broke down. With Claude Sonnet 3.5, instruction-following improved enough that Athena could reliably execute multi-step workflows described in plain English, eliminating the need for custom model training. With Sonnet 4.5 and Opus 4.5, the platform began planning, executing, adapting, and delivering end-to-end professional workflows. Sonnet 4.5 handles the bulk of autonomous workflows, while Opus 4.6 takes on harder problems like multi-entity financial modeling, deep legal research across large document sets, and compliance workflows that require high precision.
Athena built on Claude's API without any model-specific integration code. A new model version is a config update: validate against the test suite, update the model pointer, deploy. When Sonnet 4.6 launched, it was live on Athena within 30 minutes with no retraining, re-prompting, or downtime. "That's only possible because Claude's out-of-the-box instruction-following is strong enough that we've never needed brittle fine-tuned layers on top of it," said Geils.
The results: Agentic workflows at scale
For Athena's customers, work that previously took days now takes hours. Workflows are configured using natural language system prompts, so business users can set up and adjust them without developers. At a Fortune 500 company, legal, HR, and finance teams had invested millions in competing platforms that couldn't deliver insights from fragmented legacy systems. Athena deployed a custom workflow solution, multi-department KPI dashboards, and a CEO productivity agent in four weeks, replacing a competing platform that had required 12+ months of implementation. The agent pulls from email, calendar, CRM, and financial reporting systems simultaneously to produce a daily executive briefing, all configured through natural language prompts rather than custom code.
At an AmLaw 100 law firm, Claude now generates and distributes all 400 monthly compensation memos end-to-end. The previous process, which involved Excel macros, individual PDFs, and manual drops into secure SharePoint folders with a two-person verification check, took 2–3 days every month. It now runs overnight with zero manual intervention.
A global food and beverage company came to Athena, with business units being renamed, databases being merged, and legacy systems full of conflicting records. A prior AI platform had failed on the same environment because it needed clean, consistent data to function. "Last year, we would say 'you need to clean your data first,'" said Geils. "Now, the agents are better than humans at handling messy data."
A leading global professional services firm went to production across multiple practice areas in eight weeks. "Athena's not a tool anymore,” said a technology lead at the firm. Across the platform, Athena's customers are now collectively running over 10,000 autonomous workflows every week.
Athena’s goal is proving AI can be “powerful, trustworthy, and genuinely useful at the world's most complex organizations,” Geils said. “Every improvement in Claude directly accelerates our product roadmap and improves customer outcomes.”
"We needed a model that could reason through ambiguity, follow complex instructions, and explain its reasoning within strict compliance frameworks. Claude was the best fit."
Founder and CEO, Athena Intelligence
Video caption