Agent framework that generates its own topology and evolves at runtime

Agents automatically discover, instantiate, and wire themselves into a runtime topology without explicit configuration. The framework uses reflection and capability introspection to map agent dependencies, then constructs a directed acyclic graph (DAG) of agent relationships at startup. Agents register their input/output schemas, and the framework matches compatible producers and consumers, creating inter-agent communication channels dynamically based on type compatibility and declared capabilities.

The framework monitors agent performance, error rates, and communication patterns during execution, then dynamically modifies the topology by spawning new agents, removing underperforming ones, or rewiring connections. Uses metrics collection and heuristic-based decision rules to detect when topology changes would improve throughput or reliability. New agents are instantiated from templates or learned patterns, and the system performs live rewiring without stopping the existing agent network.

Records detailed execution traces of agent interactions including inputs, outputs, intermediate states, and timing information. Provides tools to inspect traces, replay specific execution paths, and debug agent behavior. Supports breakpoints and step-through debugging for interactive troubleshooting. Traces can be exported for analysis or shared for collaborative debugging.

Enforces resource limits (CPU, memory, API calls, token usage) per agent or agent group using quota systems. The framework monitors resource consumption and throttles or terminates agents that exceed limits. Supports hierarchical quotas where parent agents have budgets that are subdivided among child agents. Integrates with cloud resource managers (Kubernetes, AWS Lambda) for automatic scaling.

Supports multiple versions of agents running simultaneously, with traffic routing to specific versions based on rules (e.g., 10% to new version, 90% to stable). Enables gradual rollout of agent updates with automatic rollback if error rates exceed thresholds. Tracks version history and allows pinning agents to specific versions for reproducibility.

Agents communicate through a capability-aware message broker that routes messages based on declared input/output schemas and capability tags rather than explicit routing rules. The framework maintains a capability registry mapping agent IDs to their input/output types, and uses this to determine valid message recipients. Messages include capability requirements in their headers, allowing the broker to find compatible agents dynamically and handle fan-out to multiple agents if needed.

The framework automatically inspects agent class definitions, method signatures, and decorators to extract input/output schemas and capability metadata without requiring manual schema definition. Uses reflection/introspection APIs (Python inspect, TypeScript reflection, etc.) to parse function signatures, extract type hints, and generate JSON Schema representations. Supports decorator-based capability tagging (e.g., @capability('data-processing')) to augment introspection with semantic metadata.

Automatically instruments agents to collect latency, throughput, error rates, and custom metrics without requiring agent code changes. Uses aspect-oriented programming (AOP) or decorator-based wrapping to inject telemetry collection around agent execution. Metrics are aggregated in-memory or sent to external backends (Prometheus, CloudWatch), and the framework exposes APIs to query performance data for topology evolution decisions.

Agents can be defined as composable templates with parameterized behavior, allowing the framework to spawn new agent instances with different configurations at runtime. Templates use a declarative syntax (YAML, JSON, or code-based DSL) to specify agent behavior, dependencies, and configuration parameters. The framework instantiates agents from templates, injects dependencies, and wires them into the topology automatically.

Enables agents to coordinate decisions and reach consensus across a distributed network using protocols like Raft or Byzantine Fault Tolerance (BFT). Agents can propose state changes, vote on decisions, and commit changes only when consensus is reached. The framework handles message ordering, timeout management, and failure recovery automatically, abstracting away distributed coordination complexity.

Automatically saves agent state at configurable intervals or on-demand, enabling recovery from failures and resumption of work. Uses pluggable storage backends (file system, database, cloud storage) to persist agent state snapshots. The framework handles serialization/deserialization, versioning, and garbage collection of old checkpoints automatically.

The framework can learn from agent execution traces to optimize routing decisions, topology changes, and agent behavior. Uses reinforcement learning or supervised learning on historical execution data to build models that predict which topology changes improve performance. Agents can also learn from feedback signals (success/failure, user ratings) to adapt their behavior over time.

Agents can call multiple LLM providers (OpenAI, Anthropic, local models) with automatic fallback if one provider fails or is rate-limited. The framework abstracts provider-specific APIs behind a unified interface, handles token counting and cost tracking, and distributes requests across providers based on latency, cost, and availability. Supports prompt caching and response deduplication to reduce API calls.