Agentkernet is built for teams that need more than prompting. It provides the execution primitives and control surfaces required to run multi-step, multi-model, enterprise AI applications safely and predictably.
Each service can be extended, constrained, or integrated into the way your organization already governs software and data systems.
Convert high-level objectives into task graphs with dependencies, checkpoint logic, fallback paths, and verifiable outputs.
Distribute work across planning, execution, verification, and fallback models according to the rules you define.
Control when tasks run, what can run in parallel, how retries behave, and how long-running sessions are resumed.
Define how short-term context, durable memory, retrieval state, and evidence stores are shaped over time.
Expose business systems as validated tools with typed inputs, constrained outputs, and well-defined permissions.
Track what happened, why it happened, and whether it complied with the policies defined for your environment and use case.
Teams can adapt model roles, queueing rules, memory policies, tool permissions, and approval workflows to each domain while keeping one shared runtime foundation.
kernel: objective_mode: decompose scheduler: max_parallel_tasks: 8 retry_policy: bounded-exponential human_approval_for: [write_customer_data, submit_financial_action] models: planner: enterprise-planner executor: frontier-general verifier: domain-checker memory: session_store: redis durable_store: pgvector retention_policy: policy-controlled tools: allow: [crm.lookup, ticket.update, warehouse.query] block: [erp.payment.release] observability: trace_model_calls: true trace_tool_io: true export_sink: enterprise-observability
Agentkernet is designed to give technical leaders leverage over how agents behave across different environments, teams, and risk profiles.
One kernel layer lets multiple product teams align on execution contracts, safety gates, telemetry, and tool access without blocking domain flexibility.
Use private, hybrid, or controlled hosted deployments based on the data sensitivity and operational posture required by the business.
Define different rules for sandbox, staging, and production so high-risk tools and writes are never governed by convention alone.
The product is aimed at mid-sized and large enterprises that need durable AI infrastructure, not isolated prototypes.
Stop rebuilding planning, queueing, retries, memory, and tool adapters across every AI workflow.
Use retries, checkpoints, verifiers, memory policies, and fallback models to keep long-running agent behavior stable.
Apply policy and approval requirements across tools, writes, escalations, and memory updates in a consistent way.
Make it possible to debug, tune, and explain agent behavior with first-class runtime traces and execution telemetry.
Agentkernet is intended for organizations building complex AI applications with multiple models, multiple tools, and strict operational requirements.
