AI agent development
Build agent systems around real work
Mahmud Al Muhaimin is an AI agent developer based in Dhaka, Bangladesh, designing backend APIs and task workflows informed by building a multi-agent task mesh.
What an agent backend needs
Agent task lifecycles
Model how work is published, claimed, submitted, verified and settled so each transition has a clear state and API boundary.
Backend APIs and persistence
Connect agent workflows to practical REST APIs and data stores, with inspectable records for tasks and outcomes.
Evaluation and failure paths
Plan for retries, incomplete work and verification before describing an agent workflow as reliable.
Product integration
Build the application surfaces around the agent, including the user-facing workflows needed to configure and review work.
Built and measured
SuprBuild Mesh explores agent-to-agent task coordination with Node.js, Express and SQLite. Its implementation includes more than 20 REST endpoints and was tested with 10+ concurrent agents. Local load tests measured about 120 ms median response time; this is a local result, not a production service guarantee.
Scope a project
Share the agent task, users, existing systems and constraints. We can define a small, testable first milestone before implementation.
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