Camunda 8.8 introduced AI agent coordination for your customers.
What does the agent catalog inside your own engineering org look like?
Agently is the Enterprise Workflow Intelligence Platform that gives engineering leadership a live inventory, audit trail, and governance layer for every AI agent running internally — the same discipline Camunda brings to customers, applied internally.
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About Teqfocus
The partner that owns the data layer and the application layer — and operates what it deploys
Most data platform engagements deliver a pipeline. Teqfocus delivers agents that run on it — and keeps them performing through AgentOps managed services.
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Why we're reaching out to Camunda now
The 8.8 launch signals an active internal AI footprint. The governance infrastructure to match it probably isn't there yet.
Camunda 8.8's AI agent coordination features signal an active internal AI build
A company that ships AI agent coordination capabilities has an engineering org actively evaluating, building, and deploying AI tooling internally. That footprint is almost certainly fragmented — GitHub Copilot on some teams, Claude or GPT-4 APIs on others, internal agents built on Camunda's own orchestration layer in a third group. The 8.8 launch is the signal that the internal AI complexity has reached the point where governance becomes a real question.
No AI catalog means no answer to the compliance question when it arrives
Ask your engineering leadership to name every AI agent running in production internally today — the tools, the models, the data access scope, the team that owns each one. If that answer takes more than 30 seconds, or if it requires a Slack poll across engineering managers, the governance gap is real. When GRC, legal, or a regulated enterprise customer asks "what AI tools were used on this engagement," the answer needs to come from a system — not from memory.
The governance irony: Camunda sells process discipline externally, without it internally for AI
Camunda's customers trust the platform precisely because it brings structure, auditability, and control to processes that would otherwise be opaque. That positioning — workflow governance as a competitive advantage — applies directly to the AI agent sprawl inside Camunda's own engineering organization. Running Agently internally isn't just a governance story. It's the product dogfood argument.
"The company that teaches enterprises how to govern their workflows may be running its own AI deployment without a catalog, an audit trail, or a single source of truth for what's deployed."
What this means for your team
Three capabilities Agently delivers that directly address Camunda's internal AI governance gap
Agent Catalog — a live inventory of every agent your engineering org is running
The catalog problem is deceptively simple to state and surprisingly hard to solve without infrastructure. Agently's Agent Catalog is the starting point: a live inventory of every agent in the engineering org, its owner, its data access scope, the model it runs on, and its last audit date. For a company that will increasingly be asked by regulated enterprise customers to describe its internal AI governance practices, this is the defensible answer. Building it from a Slack poll every quarter is not.
Audit trail — AI usage that's reportable by design, not reconstructed after the fact
When your legal or compliance team asks "what AI tools did the engineering team use on this project," the answer should come from a system. Agently provides the workflow intelligence layer and audit trail that makes AI usage reportable — not just defensible in conversation, but actually queryable. For a company selling process governance to regulated enterprises and increasingly subject to AI usage questions from procurement teams, this infrastructure is the credibility layer.
Workflow Intelligence — see where agents duplicate work, where handoffs create latency
Agently surfaces where AI agents across teams are duplicating work, where handoffs between agents create latency in the development pipeline, and where the process model your teams intended diverges from what's actually executing. For Camunda's engineering org — which builds process intelligence tooling and therefore has unusually high standards for its own operational visibility — running Workflow Intelligence internally is both practically useful and philosophically consistent. You build this for customers. Running it yourself closes the gap.
Proof of work
How we've done this before
Enterprise Software — Internal AI Governance Deployment
An enterprise software company with a large engineering organization had AI tooling deployed across multiple teams — copilot tools, model API integrations, and internal agents built on their own platform. There was no central catalog of what was running, no audit trail for compliance purposes, and no visibility into where agents were duplicating work or creating handoff latency. GRC flagged the gap during an enterprise customer audit.
We deployed Agently as the internal AI governance layer: Agent Catalog for full inventory with ownership, data access scope, and model attribution; audit trail infrastructure for compliance reporting; and Workflow Intelligence dashboards surfacing duplication and handoff latency across engineering teams. The deployment was scoped to the engineering organization first, with a framework to extend to product and operations.
Engineering leadership could answer the "what AI tools are we running" question from a live system rather than a manual audit within the first sprint. The audit trail framework satisfied the GRC requirement that had originally flagged the gap. Workflow Intelligence surfaced three cases of duplicate agent work across teams that had developed independently — consolidation reduced maintenance overhead across those workstreams. [VERIFY specific metrics with delivery team before publishing]
Technology Partners
Teqfocus brings Salesforce Summit, Snowflake Premier, AWS Advanced, and Databricks credentials — the right tool for the right layer, without single-vendor lock-in.
- Summit Consulting Partner
- 200+ Certified Experts
- Sales Cloud, Agentforce, Data Cloud
- Agentforce deployments for Hi-Tech enterprises
- Premier Services Partner
- 20+ SnowPro Certified
- 50+ customers
- Cortex Agents architecture, dbt governance, and data fabric design
- Certified Consulting Partnert
- 20+ AI & data workloads
- MLOps pipelines
- AI governance frameworks and model monitoring
- Advanced Consulting Partner
- Data & Analytics Competency
- 150+ active engagements
- Cloud architecture for enterprise data platforms
Worth a 30-minute conversation?
If internal AI governance — a catalog, audit trail, and workflow intelligence layer — is on your engineering org's agenda, the 30 minutes is worth it.
