AutoGen Limitations and Common Issues

Token Cost Scaling

The most impactful limitation in AutoGen is how token costs scale with conversation length and agent count. Every message in a multi-agent conversation becomes part of the shared context that all subsequent agents must process. This means token consumption grows quadratically rather than linearly as conversations progress.

Consider a group chat with five agents. When the tenth message is sent, all nine previous messages are included as context. When the twentieth message is sent, all nineteen are included. By the thirtieth message, each agent call processes the entire conversation history, which can easily exceed 50,000 tokens per call. Multiply that by five agents and thirty turns, and a single task can consume over a million tokens.

AutoGen does not include built-in conversation summarization or context window management. Developers must implement their own strategies for compressing conversation history, such as periodic summarization steps that replace older messages with condensed summaries. Without these custom implementations, costs escalate unpredictably and conversations can exceed the model's context window entirely, causing failures.

The practical workaround is to design conversations with strict turn limits, use cheaper models for agents that handle routine tasks, and implement manual summarization checkpoints. The Microsoft Agent Framework adds configurable summarization strategies that handle this automatically, which is one of the strongest reasons to migrate for cost-sensitive applications.

Debugging and Observability

When a multi-agent conversation produces incorrect or unexpected results, diagnosing the root cause is genuinely difficult. The failure might originate in any agent's system message, the conversation flow logic, a tool call that returned unexpected data, the model's interpretation of ambiguous instructions, or the interaction between multiple agents' outputs.

AutoGen provides basic logging that records the messages exchanged between agents, but it lacks structured observability tooling. There are no built-in trace IDs for following a request through multiple agents, no performance metrics for identifying bottlenecks, no error categorization for grouping similar failures, and no visualization tools for understanding conversation flow patterns.

Developers typically resort to printing full conversation logs and manually reading through them to find where things went wrong. For simple two-agent conversations this is manageable, but for group chats with five or more agents and dozens of turns, manual log analysis is time-consuming and error-prone. Intermittent failures caused by LLM non-determinism are especially difficult to reproduce and diagnose.

The Microsoft Agent Framework improves this significantly with OpenTelemetry integration that provides distributed tracing, structured logging, and metric collection. Application Insights on Azure can visualize agent conversation flows, track latency per agent, and alert on error rate thresholds. For teams that need production-grade observability, this is a compelling reason to migrate.

State Management Gaps

In AutoGen, the conversation history is effectively the only state. There is no built-in mechanism for persisting agent state between sessions, creating checkpoints during long-running workflows, rolling back to a previous state when something goes wrong, or branching conversations for parallel exploration of different approaches.

This means that if an agent system crashes or times out during a complex multi-step task, all progress is lost. There is no way to resume from where the conversation left off. Developers must implement their own persistence layer, which typically involves serializing the conversation history to a database and rebuilding the agent state from that history when resuming.

Long-running workflows are particularly affected. A data analysis task that takes thirty minutes of agent collaboration has no intermediate save points. If the process fails at minute twenty-nine, the entire thirty minutes of work must be repeated from scratch. For enterprise workflows where tasks can run for hours, this lack of durability is a serious constraint.

The Microsoft Agent Framework addresses this with built-in state persistence, checkpointing, and conversation replay capabilities. Agents can save their state at configurable intervals, and the framework can reconstruct any previous state from the checkpoint history. This makes long-running and mission-critical workflows significantly more reliable.

Conversation Unpredictability

Because conversations are driven by LLM reasoning, the exact flow of a multi-agent conversation varies between runs even with identical inputs and system messages. The same task might be completed in eight turns one time and fifteen turns the next. Agents might choose different approaches, ask different clarifying questions, or produce different intermediate results.

This non-determinism creates three practical problems. First, testing is difficult because expected outputs cannot be precisely defined. Assertion-based tests that check for specific responses are fragile, and evaluation-based tests that assess quality are expensive and slow to run. Second, cost predictions are unreliable because the number of turns and tokens consumed varies between runs. Third, compliance and audit requirements are harder to meet because the system cannot guarantee it will follow the same process every time.

Temperature settings can reduce but not eliminate this variability. Even at temperature zero, different model versions, API provider implementations, and request timing can produce different outputs. For applications that require deterministic execution, graph-based frameworks like LangGraph provide stronger guarantees by defining explicit execution paths rather than relying on LLM-driven flow control.

Code Execution Risks

AutoGen's code execution capability is powerful but introduces security and reliability concerns. Agents can generate and execute arbitrary code, which means a poorly prompted agent or a model hallucination can produce code that consumes excessive resources, makes unintended network calls, modifies unexpected files, or enters infinite loops.

The sandboxing options (local execution, Docker containers, and Azure Container Instances) provide isolation boundaries, but each has tradeoffs. Local execution is fast but provides no isolation. Docker containers provide good isolation but add latency for container startup and teardown. Azure Container Instances provide strong isolation with the overhead of cloud provisioning and network latency.

There is no built-in code review or approval step before execution. If an agent generates destructive code, it executes immediately unless the developer has implemented custom approval logic. For production systems that execute generated code, implementing a human-in-the-loop approval step or at minimum a code analysis filter is strongly recommended.

Group Chat Scaling Issues

AutoGen's group chat mechanism relies on a manager agent to select which agent should speak next. This selection process itself consumes tokens because the manager must evaluate the conversation history and all agent descriptions to make its choice. As the number of agents increases, the manager's system message grows with all the agent descriptions, and the selection decision becomes more complex and error-prone.

In practice, group chats with more than five to seven agents become unwieldy. The manager increasingly makes poor speaker selection choices, agents repeat work that other agents have already completed, and conversations take longer to converge on solutions. The token cost of the manager's selection process alone can become a significant expense.

The workaround is to decompose complex tasks into smaller sub-conversations with fewer agents rather than putting all agents into a single group chat. A hierarchical architecture where a coordinator agent manages multiple smaller agent teams is more efficient and produces better results than a flat group chat with many participants.

Maintenance Mode Implications

AutoGen entered maintenance mode in October 2025, meaning it receives only security patches and critical bug fixes. No new features, performance improvements, or API enhancements will be added. The framework will continue to work with current model APIs, but as model providers evolve their APIs and capabilities, AutoGen may not keep pace.

For existing deployments, maintenance mode is not an immediate problem. AutoGen continues to function and will receive security updates. However, teams should plan their migration to the Microsoft Agent Framework on a timeline that aligns with their need for new features like improved state management, better observability, .NET support, or the Agent-to-Agent protocol.

The migration path from AutoGen to the Microsoft Agent Framework is well-documented, and the core concepts (agents, conversations, tools) map directly between the two frameworks. The effort is primarily in adapting to new API patterns rather than rethinking the architecture. Microsoft provides migration guides and the community has produced numerous examples of converted projects.