Building Autonomous Problem Solvers in AWS
Explore how to build autonomous problem solvers on AWS by combining foundation models with memory management, orchestration, and human-in-the-loop controls. Understand multi-step reasoning, state persistence, and safe agent workflows to create reliable agentic AI systems that operate effectively across complex tasks and environments.
We'll cover the following...
- What makes an AI system an autonomous problem solver?
- Memory and state management for long-running agents
- Designing resilient agent state transitions
- ReAct pattern for structured reasoning
- Controlling autonomy with human-in-the-loop workflows
- Observability and evaluation of autonomous agents
- A shift to advanced agentic systems
Autonomous problem solvers are a defining capability of modern agentic AI systems that reason over time, take actions, observe outcomes, and adjust their behavior across multiple steps. On AWS, this autonomy is not achieved by a single foundation model alone, but through orchestration patterns that combine models, stateful services, and governance controls.
Let’s explore how memory, structured reasoning, and human oversight work together to create reliable autonomous agents, setting the stage for more advanced agentic architectures later in the chapter.
What makes an AI system an autonomous problem solver?
An AI system acts as an autonomous problem solver when it can move toward a goal through multiple steps instead of producing a single response. In agentic AI systems, the agent determines the next action, plans a sequence of steps, and adjusts its behavior based on intermediate results. Single prompt-response applications lack this capability because they rely on a single model invocation and do not track prior steps or planned actions.
True autonomy is built from a set of core capabilities working together. Reasoning and planning allow the agent to decompose a problem; tool use enables interaction with external systems; memory preserves context; and state persistence ensures progress is not lost between steps. Controlled autonomy adds safeguards that let the agent know when to pause, escalate, or stop.
On AWS, these capabilities are distributed across services. Amazon Bedrock provides model reasoning, AWS Lambda executes actions, and Step Functions coordinate execution flow. Foundation models themselves are stateless and do not retain memory across invocations. Instead, memory is handled at the agent runtime layer. Services such as Amazon Bedrock Agents and AgentCore manage short-term and long-term agent memory by externalizing context and state and automatically supplying relevant information to each model ...