Quick wins for production agents
Agentic workflows — systems where an LLM autonomously decides what actions to take — are powerful but surprisingly tricky to build well. The model itself is often the easy part. The hard part is everything around it: keeping users informed, surviving failures, and making the whole thing debuggable.
In this post, we’ll explore seven key areas where you can focus your efforts to make your agent production-ready. These include streaming execution feedback, running agents in background workers, making agents interruptible, managing context growth, automatic memory compression, step-level logging, and clean separation of concerns.
By addressing these challenges, you can take your agentic system to the next level. Let’s dive in and explore each of these in more detail.
1. Streaming Execution Feedback
A common issue I see: the user starts your agent and nothing happens. A spinner keeps spinning, no progress is visible, and the user simply leaves. They assume it’s broken.
You want to show intermediate steps of the agent’s reasoning and execution. Stream not just LLM tokens, but everything — tool calls, decisions, state changes. The user should see what’s happening in real time.
This is typically achieved via Server-Sent Events or WebSockets, with SSE being the simpler option.
Benefits:
- User stays engaged and trusts the system instead of abandoning it
- Easier debugging during development — you see exactly what the agent is doing
- Enables users to catch mistakes early and intervene
Costs:
Requires setting up SSE or WebSocket infrastructure. You also need to decide what events to surface — tool calls are usually the minimum, but reasoning steps can help too.
More to read:
2. Running Agents in Background Workers
If the agent state is stored only on the client or within a single request, the user loses all progress when the page refreshes or the connection drops. This is a terrible experience for anything that takes more than a few seconds.
The solution is to decouple agent execution from the request lifecycle. The user’s request should only trigger execution, not be the execution.
Benefits:
- Agent survives disconnections, refreshes, and browser closures
- User can close the tab and come back later to see results
- Enables long-running tasks without timeout issues
Costs:
You need infrastructure to run background jobs. Options include Inngest, Vercel’s useWorkflow, or a dedicated server. This adds deployment complexity, but it’s worth it for anything non-trivial.
More to read:
3. Making Agents Interruptible and Resumable
The user should be able to stop the agent mid-execution and either resume or modify the request. Often, the initial request is wrong, and users only realize this after seeing what the agent starts producing.
This means the agent must be interruptible, resumable, and idempotent.
Benefits:
- User can correct course mid-execution without starting over
- Graceful recovery from transient failures
- Better resource utilization — stop wasted work early
Costs:
Requires checkpointing state between steps. Tool calls must be idempotent, or you need rollback logic. This is where most agents fall apart — they assume happy paths.
More to read:
4. Managing Context Growth
Passing the entire conversation history to the model leads to three problems: higher API costs, increased latency, and eventually hitting context window limits.
You need RAG optimization and aggressive removal of irrelevant information from the context. Not everything needs to go to the model every time.
Benefits:
- Significantly reduced API costs
- Lower latency for each request
- Avoids context window overflow errors
Costs:
Requires a chunking strategy and possibly rerankers. There’s always a trade-off between context size and accuracy — you might prune something the model actually needed.
More to read:
5. Automatic Memory Compression
Without summarization and context filtering, the agent’s response quality degrades over time. The model gets overwhelmed with irrelevant history and loses track of what matters.
Implement automatic memory compression — summarize older turns, filter out noise, keep only what’s relevant to the current task.
Benefits:
- Sustained response quality over long conversations
- Predictable costs regardless of conversation length
- Agent “remembers” what matters without drowning in details
Costs:
You need summarization prompts and logic to decide when to trigger compression. There’s risk of losing important details if the summarization is too aggressive.
More to read:
6. Step-Level Logging
If agent steps and intermediate decisions are not logged, errors cannot be debugged or reproduced. You’ll get bug reports you can’t investigate.
Log every step with explicit inputs and outputs. Not just the final response — every tool call, every decision point, every state transition.
Benefits:
- Reproducible debugging — replay exactly what happened
- Foundation for evals and regression testing
- Audit trail for compliance and security reviews
Costs:
Storage overhead, especially for verbose agents. You need to define a logging schema upfront — ad-hoc logging becomes unmaintainable fast.
More to read:
7. Clean Separation of Concerns
When the UI is aware of the agent’s internal logic, the system becomes fragile and difficult to scale. A change in the agent breaks the UI. A UI requirement leaks into agent code.
Maintain clear separation between state (what’s stored in the database), presentation (what’s shown to the user), and execution (what the agent does internally).
Benefits:
- Easier to scale and test each layer independently
- Can swap UI or agent implementation without touching the other
- Cleaner codebase that’s easier to reason about
Costs:
Requires upfront architectural thinking. More abstractions to maintain. But the alternative — a tangled mess — is worse.
Conclusion
These are engineering wins, not ML research problems. They can be implemented in days, not months, and immediately make your agent feel production-ready. None of them require better models or fancier prompts — just solid engineering.