Seven engineering lessons for building production AI chat agents

Engineers at Trigger.dev have published a detailed retrospective on building a production-grade AI chat agent for their developer dashboard. Released in October 2026, the article outlines seven critical lessons learned while implementing features like debugging, chart generation, and documentation search. The findings focus on architectural decisions that improve user experience and system reliability.

What happened

The team built an internal tool to help users interact with their platform through natural language. This agent performs complex tasks such as inspecting run logs, generating visualizations, and querying documentation. Because these operations involve heavy lifting like sandbox execution and file access, the engineers encountered specific challenges related to latency and state management.

They identified that standard request-response patterns were insufficient for long-running agent tasks. To address this, they implemented a split architecture where an initial lightweight response is streamed immediately while the heavier agent logic initializes in the background. This approach ensures users receive immediate feedback, such as seeing a tool call like get_run("abc123") appear within a second, rather than staring at a static loading spinner.

The article also details how they handled conversation compaction, retry logic, and stream durability. By sharing their code and configuration examples, they provide a blueprint for other developers facing similar issues when moving AI prototypes into production environments where reliability and perceived performance are paramount.

How it works

The core mechanism relies on decoupling the initial user interaction from the full agent execution. When a message arrives, a standard web server route handles the first model call. This call streams a response back to the client instantly, often containing a tool call indicator. Simultaneously, the system spins up the full agent environment. Once ready, the agent takes over the conversation context, including the initial tool calls, ensuring continuity without duplicating messages or changing tones.

For data integrity, the system uses durable streams backed by object storage. Each chunk of the response is assigned a sequence number. If a client disconnects and reconnects, it sends the last received sequence ID. The server then replays any missed chunks from storage before resuming the live stream. This prevents data loss and avoids the awkwardness of restarting a long generation from scratch.

Token counting for context window management is optimized by tracking only the input tokens of the most recent step. Since output from one step becomes input for the next, summing every step’s input would lead to massive overcounting. This method ensures compaction triggers only when necessary, preserving relevant context without premature summarization.

Key details

• Split initialization: The first model call runs in the request handler for speed, while the heavy agent starts in the background, cutting time to useful update by half.
• Accurate token counting: Compaction logic uses only the last step’s input token count to avoid overcounting repeated conversation history.
• Retry limits: Tools implement internal retry counters with short backoffs for network blips, capping consecutive failures to prevent infinite loops.
• Durable streaming: Response chunks are stored with sequence IDs in object storage, allowing clients to resume interrupted streams seamlessly.
• Prompt versioning: Prompts are defined in code with version control, enabling non-deployment updates and A/B testing via a dashboard.
• Background evals: Ten percent of completed turns are sent to a judge model for scoring on groundedness, answer quality, and conciseness.

Why it matters

For software engineers building AI interfaces, perceived latency is often more critical than actual total execution time. Users abandon tools that appear unresponsive, even if the backend is working hard. The split-initialization pattern described here offers a concrete solution to this UX problem. It demonstrates that you do not need to wait for the entire agent stack to be ready before providing value to the user. Showing intermediate steps, such as tool invocations, builds trust and keeps the user engaged during longer operations.

Reliability in streaming connections is another major pain point in real-world applications. Network instability is common, especially for mobile users or those on unstable connections. Implementing durable streams with sequence-based resumption transforms a fragile HTTP connection into a robust session. This reduces support tickets and improves the overall quality of the interaction, making the agent feel like a native part of the application rather than a flaky external service.

Finally, the emphasis on observability and evals highlights the shift from experimental AI to engineered systems. Without structured evaluation and versioned prompts, iterating on agent behavior becomes guesswork. By treating prompts as code and evaluating outputs systematically, teams can make data-driven improvements. This rigor is essential for maintaining high-quality responses as the complexity of tools and user queries grows.

What you can do

• Implement a two-stage response handler where the first call streams immediate feedback while the main agent initializes in the background.
• Audit your token counting logic to ensure you are not summing inputs from every historical step, which leads to premature context compaction.
• Add sequence IDs to your streaming responses and store chunks in durable storage to enable client-side resumption after disconnects.
• Define explicit retry limits within your tool definitions to prevent agents from looping indefinitely on transient errors.
• Version your prompts in code and create a mechanism to switch versions without redeploying the entire application.
• Set up background evaluation jobs that score a sample of conversations using a judge model to track performance trends over time.