If you have built a working agent — a model deciding, calling a tool, observing, and looping — you know the basic loop is the easy part. The hard part is everything that happens when the loop meets reality: long tasks that exceed the context window, tools that fail mid-run, multiple agents that need to coordinate, and inputs that look nothing like your test set. This is where most agents that demoed beautifully fall apart.
This guide assumes you already understand the fundamentals and want the depth that separates a prototype from a production system. We will cover memory architecture, error recovery, multi-agent patterns, context management, and the edge cases experienced builders learn to anticipate. If any of the basics feel shaky, the step-by-step guide is the better starting point.
The recurring theme is this: advanced agent work is mostly defensive engineering. The model is capable; your job is to contain its failures and stretch its limits without letting either break the system.
Memory Architecture
A basic agent forgets everything between runs and crams the entire task into one context window. Real tasks outgrow both limits fast.
Working memory vs. persistent memory
Working memory is the current context — the running conversation and recent tool results. Persistent memory survives across runs, stored externally and retrieved when relevant. Conflating the two is a classic mistake. Keep the context lean and push durable facts to a store you query deliberately.
Retrieval as memory
For agents that need to recall large bodies of information, store it externally and retrieve only the relevant slice per step. This keeps the context window from overflowing and keeps each model call focused. The trade-off is that retrieval quality becomes a dependency — bad retrieval starves the agent of what it needs.
Summarization to compress history
On long-running tasks, periodically summarize the history so the context stays within limits. The risk is that summarization discards a detail the agent needs later. Summarize conservatively and keep critical facts verbatim.
Error Recovery That Actually Recovers
Beginner agents treat a tool error as a dead end. Advanced agents treat it as information.
- Feed errors back as observations. When a tool fails, return the error message to the model so it can adjust, rather than crashing the loop.
- Distinguish retryable from fatal. A timeout is worth retrying; a malformed-request error means the agent should change its approach. Encode this distinction.
- Cap retries per step. Without a per-step retry limit, an agent can burn its entire step budget retrying one broken tool.
- Detect loops. Watch for the agent repeating the same action with the same result and break out deliberately.
These recovery patterns are what our metrics guide calls recovery rate, and improving them is among the highest-leverage work you can do.
Multi-Agent Coordination
When one agent is not enough, you reach for multiple — but coordination introduces its own failure modes.
Orchestrator and workers
A common pattern puts one orchestrator agent in charge of decomposing a task and delegating subtasks to specialized worker agents. The orchestrator integrates the results. This keeps each agent's job narrow, which improves reliability, at the cost of coordination overhead.
The communication problem
Agents communicate through text, and text is lossy. An orchestrator that delegates ambiguously gets back work that misses the point. Be explicit in delegation and validate worker outputs before integrating them. Most multi-agent failures are communication failures, not capability failures.
When not to use multiple agents
Multi-agent systems are seductive and usually premature. A single well-designed agent with good tools beats a swarm of poorly coordinated ones. Reach for multiple agents only when subtasks are genuinely independent and specialized. Our trade-offs guide covers this temptation directly.
Context Management Under Pressure
The context window is a hard constraint, and managing it well is an advanced skill in itself.
Token budgeting
Treat the context as a budget. The system prompt, tool definitions, history, and retrieved data all compete for space. On complex agents, deliberately allocate this budget rather than letting history grow unchecked until it overflows.
Pruning irrelevant history
Not every past step matters to the current decision. Pruning stale tool results and resolved subtasks from the context keeps the model focused and the cost down. The judgment of what to prune is where experience shows.
Edge Cases Experienced Builders Anticipate
The difference between a prototype and a production agent is often just a list of anticipated edge cases.
- The empty result. A tool returns nothing. Does the agent handle it, or assume failure and spiral?
- The ambiguous goal. The user's request can be read two ways. Does the agent ask, or guess and commit?
- The adversarial input. Someone tries to redirect the agent through its inputs. Is the agent hardened against instruction injection?
- The partial success. The agent did most of the task but not all. Does it report honestly or claim full success?
Each of these is a place where the demo passes and production breaks. Our risks guide treats the security-flavored edge cases in more depth.
Planning Strategies
How an agent plans its steps determines how it handles complex tasks, and there is real depth here.
Plan-then-execute versus react
In a react-style loop, the agent decides each step based on the latest observation, never committing to a full plan. This adapts well to surprises but can lose the thread on long tasks. In plan-then-execute, the agent drafts a complete plan up front, then carries it out. This stays coherent over long tasks but handles surprises poorly when reality diverges from the plan. Mature agents often blend the two: draft a rough plan, execute reactively, and re-plan when observations contradict the plan.
Decomposition
For hard tasks, having the agent explicitly break the goal into subgoals before acting improves reliability. The decomposition becomes a checklist the agent works through, which keeps it from getting lost. The risk is a bad decomposition that locks in the wrong approach, so allow the agent to revise its breakdown as it learns.
Reflection
Some advanced agents pause to critique their own progress before continuing. A reflection step — asking the agent to evaluate whether its last action actually helped — catches mistakes the forward-only loop would compound. It costs extra calls, so reserve it for high-stakes or long-running tasks where the cost is justified.
Frequently Asked Questions
When should I move from one agent to multiple?
Only when subtasks are genuinely independent and benefit from specialization. Multi-agent systems add coordination overhead and a new failure surface — lossy text communication between agents. A single well-equipped agent usually outperforms a poorly coordinated swarm, so treat multiple agents as a last resort.
How do I keep a long task within the context window?
Combine external memory with retrieval and periodic summarization. Push durable facts to a store and retrieve only the relevant slice per step, and summarize resolved history to free space. Keep critical details verbatim, since aggressive summarization can drop something the agent needs later.
What is the most important error-recovery pattern?
Feeding errors back to the model as observations rather than crashing. This lets the agent adapt its approach. Pair it with a distinction between retryable and fatal errors and a per-step retry cap, so the agent recovers intelligently without burning its step budget on one broken tool.
How do I prevent an agent from looping forever?
Detect repetition and enforce a step cap. Watch for the agent taking the same action with the same result and break out deliberately when it does. The hard step cap is the backstop that guarantees termination even when loop detection misses.
What edge case breaks agents most often in production?
Ambiguous goals and adversarial inputs. Demos use clean, well-formed requests; production sends messy, ambiguous, and occasionally hostile ones. An agent that guesses on ambiguity or follows injected instructions will fail in ways the demo never revealed.
Key Takeaways
- Separate working memory from persistent memory, and use retrieval plus summarization to handle long tasks.
- Treat tool errors as observations the agent can act on, distinguishing retryable from fatal failures.
- Use multi-agent orchestration only when subtasks are independent; most multi-agent failures are communication failures.
- Manage the context window as a budget, pruning stale history to stay focused and cheap.
- Anticipate edge cases — empty results, ambiguity, adversarial input, partial success — that pass in demos and break in production.