Agent Runtime & Orchestration
AI agent orchestration for multi-agent systems and AI agent workflows that plan, call tools, wait, fail and resume. This layer runs that work as a controlled, recoverable process, so a long task survives errors, restarts and approvals.
What is this layer about
An agent is not one model call. It is a loop: read the goal, plan the next step, call a model or a tool, look at the result, and decide what to do next. That loop can run for seconds or for days, and it touches many systems along the way.
The agent runtime is what keeps that loop honest. It holds the state of the task, decides which model or tool handles each step, and applies timeouts, retries and fallbacks when something goes wrong. When several agents share a job, it coordinates who does what and in which order.
Without this layer, agent behavior lives in application code and prompt text. A crash loses the work, a stuck step blocks everything, and nobody can say what the agent was doing. The model is rarely the problem; missing control over the loop is.
What do we deliver
Task loop execution
Run the plan, act, observe cycle as managed steps with limits on iterations, time and spend, so an agent cannot loop forever or wander beyond its brief.
Durable state and checkpoints
Save progress after each step so a task can resume after a crash, a deploy or a long wait, from where it stopped rather than from the beginning.
Planning and task decomposition
Break a goal into ordered steps with dependencies, and revise the plan when a step fails or new information changes what the task needs.
Multi-agent orchestration
Coordinate specialist agents on one job: hand off work, pass context, run steps in parallel where safe, and keep one clear record of who did what.
Model and tool routing decisions
Choose the model or tool for each step by task type, risk and cost, within the rules set by policy, and record why that choice was made.
Retries, timeouts and human pauses
Apply retries with backoff, timeouts and fallbacks to failing steps, and stop at defined points for human approval before the workflow continues.
Technology stack
- Agent frameworks
- LangGraph
- Microsoft Agent Framework
- CrewAI
- OpenAI Agents SDK
- Claude Agent SDK
- Durable execution
- Temporal
- AWS Step Functions
- Azure Durable Functions
- Restate
- Inngest
- State and checkpoint stores
- PostgreSQL
- Redis
- Amazon DynamoDB
- Azure Cosmos DB
- Queues and events
- Apache Kafka
- RabbitMQ
- Amazon SQS
- Azure Service Bus
- NATS
- Retry and resilience
- tenacity
- Polly
- Resilience4j
- Envoy
- LiteLLM
These are typical kinds of building blocks for an agent runtime; the choice of each stays with the customer.
Business outcome
Long tasks finish instead of restarting. A failure, restart or slow dependency no longer wipes out the work done so far. Agents resume from saved state, which makes long workflows practical to run in production.
People stay in the loop where it counts. Approval points are built into the workflow, so sensitive steps wait for a named person and the agent continues only after a decision is recorded.
Runaway agents are contained. Limits on steps, time and spend, plus clear fallbacks, keep a confused or stuck agent from running up cost or acting outside its brief.
Complex work becomes explainable. Each plan, handoff and routing decision is kept as part of the task record, so teams can answer what the agent did, in what order, and why.
Frequently asked questions
What does the agent runtime layer cover?
It runs the agent loop as a managed process: planning, step execution, saved state, routing of each step to a model or tool, retries and handoffs between agents. It does not choose the models or define the policies.
How are agents kept within their limits?
Each task runs with set limits on steps, time and spend, plus defined fallbacks. When a limit is reached, the agent stops or hands over, so a stuck or confused agent cannot loop indefinitely or act outside its brief.
Can a person approve steps before the agent continues?
Yes. Approval points can be placed at sensitive steps. The workflow pauses, waits for a named person, records the decision, and only then resumes from its saved state, even after a long wait.
How does this layer relate to the layers around it?
It sits between context and tools. It draws on Context & Memory for what the agent knows, calls Tools & Execution to act, follows Safety & Policy Controls for the rules, and passes its record to Observability.
Questions about your AI rollout?
Talk to us about how the seven layers fit your models and data.