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agents

5 posts

An abstract 3D composition of interlocking translucent blocks representing memory fragments flowing toward a locked, owned container.
agentsinfrastructure

Agent memory is only a moat if you can export it

Agent memory becomes a business asset when you control it, can export it, know when it gets deleted, and know it trains no model. Use four contract questions to tell whether a memory layer is building your company brain or the vendor's moat.

5 min read
An abstract 3D composition of scattered nodes and threads converging toward a single control point, representing the supervision work behind a fleet of agents.
agentsinfrastructure

A DIY agent fleet costs you a week, not a token budget.

A DIY agent fleet costs more than tokens and seats because every agent needs someone to version, supervise, limit and measure it. For businesses without an engineering bench, the right build-vs-buy question is who owns that work and whether it's included in the platform.

5 min read
An abstract 3D composition showing a single glowing line branching into a network of interconnected nodes and pathways.
agentsengineering

Agentic graphs vs. loops: what actually breaks when agents run wide

Linear agents get slow when independent work is forced through one queue. Graphs help when the work is genuinely wide, as long as verification, isolation, and measurement are built into the structure.

6 min read
An abstract 3D composition of interlocking layered forms flowing into a single connected structure, representing memory and knowledge converging into one learning system.
agentsengineering

Why the Hermes agent framework is the default assistant in every Levain workspace

Levain workspaces now ship with a default assistant built on the Hermes agent framework because Hermes can keep memory, read a knowledge base, and write reusable skills. That means the assistant can learn how your business works instead of asking you to explain billing, approvals, or customer handoffs every Monday.

5 min read
Agent versions v1 through v4 converging into an upward trajectory, illustrating iterative improvement
agentsengineering

Your AI agent went live. Now the real work starts.

Agents don't improve on their own. A structured iteration loop (measure, test, refine) is what separates a v1 that stagnates from one that gets meaningfully better over time.

4 min read