Agentlake

The scaling problem

One agent, one VM, one purpose. Infrastructure cost accrues whether agents work or sit idle. Scaling requires hiring, not engineering.

The agent island problem

Without a control plane, tools operate in isolation, duplicate work, and forget everything the moment a session ends.

The governance gap

When an agent pushes a change to production, who is accountable? Without structure, that decision can't be traced, audited, or reversed.

Cognitive debt

AI-generated systems accumulate faster than teams can understand them, typically forcing a costly rebuild within 12 to 18 months.

Three-tier command hierarchy

Fleet Prime, Team Orchestrators, and Worker Agents, connected by open protocols (A2A and MCP).

Five-stage governed autonomy

A trust ladder tying agent permissions to demonstrated, measured performance.

Enterprise Memory Vault

Persistent, compounding memory on a vector database with semantic search. No more session amnesia.

Process-to-Agent assessment

A structured diagnostic that ensures agents are only deployed on workflows genuinely ready for automation.

Multi-model routing

Task-appropriate model selection that cuts inference cost by 40 to 60% without sacrificing quality.

Cloud-agnostic deployment

Kubernetes-native for multi-cloud portability, or cloud-native serverless built on AWS.

AWS-native foundation

Built on Amazon Bedrock (including Anthropic Claude and Amazon Nova) and Amazon Bedrock AgentCore.

Scale-to-zero economics

AgentCore containers start on task arrival and stop after 15 minutes idle. Empty queue, zero cost.

Named human accountability

Every AI-generated artifact traces back to a named human who validated its governing constraints.

Full observability

CloudWatch and X-Ray, or OpenTelemetry-compatible tooling, tracing every decision end to end.

IAM-native model access

Agents reach foundation models through Bedrock using standard IAM roles. No separate API keys to manage.

Regulatory alignment

Architecture maps to ISO 42001 and is designed to support EU AI Act obligations for high-risk AI systems.

Business value

From linear output to non-linear throughout

Agent Lake shifts engineering economics from output tied to headcount, to output tied to orchestration.

Metric Traditional, human-only With agent lake Change

Tasks executed per sprint. ~40/ week (10-person team). 200+/week (parallel execution). +5x
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Time per routine task. 2-4 hours. 10-20 minutes. -85%
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New feature cycle time. ~2 weeks average 5-7 days -60%
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Senior engineer time on high-value work. ~30% ~70% 2.3x
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A team of 10 engineers operating Agent Lake can deliver the throughout of a team of 20, without the recruitment overhead. Measured against a human-effort baseline, not a legacy VM deployment, to show the real impact on margins.

Proven in delivery, not just projected

Measured results real CloudNation agent-based engagements:
Schermafbeelding 2026-07-01 om 11.32.27

80+

dedicated cloud-native consultants

500+

Organizations served across financial services, manufacturing, and critical infrastructure

90-days

internal sprint validating the platform on our own live engagements, before offering it to customers

5 days

typical time to a fixed-price proposal after a qualified conversation

Context

Translating politically nuanced organizational goals into strict boundaries agents can safely execute within.

Strategic alignment

Balancing competing priorities that no model can resolve through reasoning alone.

Liability and trust

The legal accountability and regulatory sign-off that comes with engaging a partner, not a tool.

Tim Roelse, Senior Manager & Digital transformation

Ready to move beyond the pilot stage?

Start with the Agent Lake Assessment: a 4-week, fixed-price engagement that maps your highest-value workflows and gives you a concrete, ranked roadmap. A proposal is typically ready within 5 working days.

Request an Agent Lake Assessment

The governed foundation for enterprise AI agents

The problem

Stuck between "we tried a chatbot" and "AI actually runs part of our business"

The scaling problem

The agent island problem

The governance gap

Cognitive debt

The solution

Agent Lake: a governed platform for enterprise agent fleets

Without agent lake

With agent lake

How it works

From webhook to verified output, without a single poll

1

Fleet Prime routes strategically

2

Team Orchestrators decompose the work

3

Worker Agents execute

4

The Enterprise Memory Vault learns

Agents earn autonomy, they don't start with it

Key capabilities

Everything a governed agent platform needs, in one place