Your Agent Loops Forever: Designing an Escape Hatch Workflow for Multi-Tool Chains Without Hardcoding Limits

Someone on Reddit spent $47,000 because their AI agent got stuck in an infinite loop for 11 days. Eleven. Days. Their monitoring system apparently took a vacation, and their agent just kept calling tools, burning tokens, and racking up API bills like a particularly enthusiastic credit card fraudster.​

If you are building anything with AI agents and multi-tool orchestration, you will encounter the loop problem. Not "might encounter." Will. Your agent will decide that calling the same search API fourteen times with slightly different phrasing constitutes "thorough research." Your tool chain will ping-pong between two functions until your token budget weeps. 

Your carefully designed ReAct pattern will turn into a ReReReReReReAct pattern that never reaches the "final answer" node.

The standard advice tells you to "set a max iteration limit" and call it a day. That advice works about as well as putting a band-aid on a severed limb. Sure, your agent stops looping, but now it just gives up halfway through legitimate multi-step tasks, returning "Agent stopped due to max iterations" when users expected actual answers.​

Building proper escape hatches for agent workflows requires understanding why loops happen, what developers actually face in production, and how to design termination logic that distinguishes between "productive iteration" and "expensive hallucination spiral."

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Why Your Agent Enters the Loop Dimension

The phenomenon has a name: Loop Drift. Your agent does not intentionally decide to waste your money. It genuinely believes it needs more information, misinterprets termination signals, or loses track of what it already tried. 

The LLM powering your agent operates on context windows and probability distributions, not deterministic state machines. When the agent's internal logic says "I should stop now" but the model generates "let me try one more search," you get drift.​

A developer on r/LangChain described their agent entering a continuous tool-calling loop without ever providing a final answer. The execution graph showed the agent node calling tools repeatedly, never transitioning to the end node. The problem emerged from how the agent interpreted "completion." 

The LLM thought each tool result required verification through additional tool calls, creating a reasoning loop with no natural exit.​

Another case on r/n8n involved an agent that mistakenly processed webhook responses as new requests. 

User asks agent to add item to shopping list, agent executes POST request successfully, receives webhook confirmation, interprets confirmation as new instruction to add item, executes another POST request, receives another confirmation, and continues until hitting timeout with ten duplicate entries. The agent lacked the context to distinguish between "tool output" and "new user input."​

Complex agents consuming tool-calling capabilities use 5-20x more tokens than simple chains specifically because of loops and retries. 

When you are paying per token and your agent decides that "research" means calling the search API forty times, your infrastructure costs balloon. 

One team noted their agents cost 10x more than necessary because every tool call dumped thousands of tokens of context that the agent never actually used.​

The Development Hell Nobody Warns You About

Building your first multi-tool agent feels straightforward. You wire up LangChain or a similar framework, define a few tools, write a system prompt telling the agent what to do, and run it. The demo works beautifully. Then you deploy it, and reality arrives with a baseball bat.

Context Loss and Action Amnesia

A GitHub issue described an agent repeatedly executing the same action in an infinite loop, apparently losing track of previous actions. 

The root cause traced to context management. As the conversation grew longer, earlier tool calls and results got pushed out of the context window. The agent genuinely could not remember that it already tried that exact action three iterations ago, so it tried again. And again. And again.​

The fix required implementing explicit state tracking outside the LLM context. Instead of relying on the model to remember "I already searched for this," the system maintained a separate log of attempted actions. 

Before allowing the agent to call a tool, the orchestrator checked whether an identical or highly similar action occurred recently. If yes, block the call and force the agent to try a different approach or terminate.

Oh man, the number of times I've been there. Even in my code demos, when I see the infinite loops on a Colab output section, at least I'm glad it's working (on open-source models for free haha).

Iteration Limits That Break Everything

Multiple developers on r/n8n and other forums reported the same frustration: setting max iterations to prevent infinite loops caused their agents to fail on legitimate multi-step tasks. Set the limit to 3, and the agent times out before completing complex workflows. 

Set it to 10, and the agent burns through your API quota on simple tasks that should take 2 steps.​

One developer mentioned trying to process tasks sequentially through multiple epics, with the agent reading tickets, completing work, marking tasks finished, and moving to the next. The agent got stuck in a loop where it could not progress beyond the first task. 

The hard iteration cap prevented the agent from working through the full sequence, while removing the cap let it spiral into repetitive actions on the same task.​

The problem stems from treating all iterations equally. Iteration 1 where the agent calls a search tool differs fundamentally from iteration 8 where the agent calls the exact same search with the exact same query. 

The first represents progress. The second represents confusion. Hard caps cannot distinguish between them.

Tool Description Failures

Agents rely heavily on tool descriptions to decide which tool to use and when to stop using tools. Vague or incomplete descriptions cause agents to pick wrong tools or misunderstand when a tool's output satisfies the task requirements. 

A developer noted that refining tool descriptions often solved cases where agents consistently picked incorrect tools or called tools unnecessarily.​

The debugging process revealed that agents interpret tool descriptions literally and simplistically. A tool described as "searches the database" will get called whenever the agent thinks searching might help, even if previous searches already returned complete results. 

A tool described as "retrieves user information" might get called multiple times if the description does not specify "returns all available user information in one call."

Better tool descriptions include explicit guidance about when the tool should and should not be called, what completeness looks like for that tool's output, and whether the tool should ever be called multiple times in one session.

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Loop Guardrails: External Enforcement That Actually Works

The core principle: the system running the agent, not the agent itself, enforces termination. Trusting the agent to stop itself invites disaster. LLMs make probabilistic decisions. 

Probability means sometimes they decide not to stop even when they should. External guardrails override the agent's faulty internal logic.​

Maximum Iteration Limits Done Right

Yes, you still need max iteration limits, but implement them with nuance. Set different limits for different task types. 

Simple question-answering gets a limit of 5. Research tasks get 15. Complex multi-step workflows get 30. Track what percentage of tasks hit their limits. 

If 40% of research tasks max out at 15 iterations, either increase the limit or investigate why those tasks require so many steps. It could be prompting errors, MCP issues, content issues, the wrong model for the task, or something you just didn't pay enough attention to.​

Google's Agent Development Kit implements iteration limits through the LoopAgent pattern with max_iterations parameter, but crucially also allows agents to signal early completion by setting escalate=True in their EventActions. 

The agent can exit before hitting max iterations if it determines the quality threshold is met. This combines hard limits (preventing runaway loops) with intelligent early stopping (preventing unnecessary token waste on already-complete tasks).​

Monitor iteration counts by task category and user. If one user's tasks consistently hit max iterations while others complete in 3-5 steps, investigate whether that user's requests are genuinely more complex or whether something in their phrasing confuses the agent. 

If a specific task category always maxes out, redesign the workflow or increase the limit for that category specifically.

Repetitive Output Detection

Your agent calling the search tool five times would be fine if each search used different queries and returned different results. 

The problem emerges when the agent calls search with nearly identical queries, gets nearly identical results, and still calls search again. Detecting repetition prevents this waste.​

Implement similarity checking between consecutive actions. Before allowing a tool call, compute similarity between the proposed action and the last 2-3 actions. If similarity exceeds a threshold (typically 0.85-0.95 depending on your use case), block the action and inject a message into the agent's context: "You recently attempted a very similar action. Try a different approach or provide your final answer."

The similarity check needs to account for both the tool being called and the parameters passed to that tool. Calling search("weather in Paris") followed by search("Paris weather") represents high similarity. Calling search("weather in Paris") followed by search("population of Paris") represents low similarity even though both use the search tool.

Some frameworks call this "action deduplication." Others call it "repetition detection." Whatever the name, it prevents the most common and expensive loop pattern: the agent convinced it needs slightly different phrasing to get better results, burning tokens and tool calls on semantically identical requests.

Resource Usage Monitors

Track token consumption, API calls, and execution time in real-time. Set absolute thresholds that trigger hard stops. 

If any single agent session burns through 50,000 tokens, something went wrong. 

If a session makes more than 100 API calls, something went wrong. 

If execution time exceeds 5 minutes for a task that should take 30 seconds, something went wrong.​

These thresholds act as circuit breakers. When tripped, they kill the agent session immediately, save whatever partial results exist, log the failure for analysis, and return an error to the user. 

Better to fail fast with a clear error than drain your budget while the agent chases its tail.

Resource monitoring also enables per-user rate limiting. If a user (accidentally or maliciously) triggers agent sessions that consistently hit resource limits, you can throttle their requests or require manual approval before executing high-resource tasks. 

This prevents one problematic user from affecting your entire system's cost and performance.

Semantic Completion Checks

The hardest but most effective guardrail: programmatically validate whether the agent's output actually satisfies the task requirements. 

This requires defining completion criteria separate from the agent's own judgment.​

For structured tasks, completion criteria are straightforward. "Retrieve user's email address" is complete when the output contains a valid email address. 

"Search for recent papers on topic X" is complete when the output contains at least 3 papers published in the last 2 years.

For open-ended tasks, completion criteria become trickier. One approach uses a separate evaluator LLM that reviews the agent's output against the original request and scores completeness. If the score exceeds a threshold, trigger termination even if the agent thinks it needs more iterations. 

This adds token cost for the evaluator, but prevents much larger costs from unnecessary continued iteration.

Microsoft's Semantic Kernel implements this through termination functions that examine the last message and determine satisfaction. 

The termination function receives the conversation history, applies custom logic (which can include LLM calls, rule checks, or heuristics), and returns a boolean. If true, the agent chat loop terminates. If false, the loop continues up to max iterations.​

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Architectural Patterns That Prevent Loops Before They Start

Good architecture makes loops less likely in the first place. Several patterns emerged from production systems that successfully manage multi-tool agents without constant loop disasters.

The Orchestrator Pattern

Instead of one agent with access to all tools, use an orchestrator agent that delegates to specialized tool agents. 

The orchestrator analyzes the request, decides which specialist to call, sends the request to that specialist, receives the result, and decides whether to call another specialist or return the final answer.​

This pattern naturally limits loops because each specialist has one focused responsibility and a narrow tool set. The specialist cannot wander into irrelevant tool calls because it only has tools relevant to its domain. 

The orchestrator cannot loop infinitely through specialists because you implement loop detection at the orchestration level, tracking which specialists were already consulted.

The separation also improves debugging. When a loop occurs, you can immediately see whether it happened within a specialist (indicating a tool selection problem) or at the orchestrator level (indicating a delegation logic problem). 

Logs show clear delegation paths: Orchestrator → Search Specialist → Orchestrator → Data Specialist → Orchestrator → Done. Loops manifest as repeated patterns in this delegation chain.

Multiple agents communicating with the orchestrator, each with its own prompt templates, own tools, and custom constraints. This requires careful design, but it's worth it.

State Machines Over Freeform Reasoning

LangGraph and similar frameworks let you define agent workflows as explicit state machines with defined nodes and edges. The agent can only transition between predefined states, and each transition requires specific conditions to be met. 

This constrains the agent's action space and makes loops easier to detect and prevent.​

A state machine for a research task might include states: 

START → SEARCH → EVALUATE_RESULTS → REFINE_QUERY → SEARCH (max 2 times) → SYNTHESIZE → END. 

The agent cannot loop endlessly through SEARCH because the state machine definition only allows 2 transitions from REFINE_QUERY back to SEARCH. 

After the second attempt, the only valid transition is SYNTHESIZE → END.

Building state machines requires more upfront design than freeform ReAct agents, but the investment pays off in production. Your agent becomes more predictable, easier to test, and substantially less likely to discover creative new ways to waste money. 

The state graph visualization also makes explaining the agent's behavior to non-technical stakeholders trivial. Show them the graph, point to where the agent is stuck, and discuss whether the graph design needs adjustment.

The Approval Tool Pattern

For agents performing consequential actions (database writes, API calls to external services, financial transactions), implement an approval tool that pauses execution and waits for human confirmation. 

The agent can reason about what action it wants to take, but before executing that action, it calls approval_tool with a description of the intended action and waits for a human to approve or reject.​

This pattern prevents catastrophic loops where an agent repeatedly performs the same destructive action. The human sees "Agent wants to delete user record (attempt 7)" and realizes something went wrong. 

Without the approval step, the agent might delete the same record seven times (or attempt to, generating errors that it then tries to "fix" with more deletions).

Approval tools introduce latency and require humans in the loop, making them impractical for high-throughput or real-time systems. 

They work well for administrative agents, data management agents, or any agent where errors are expensive and speed is less critical than correctness, which is true 75% of the time.

Circuit Breaker Tool

Implement a special tool that agents can call to signal "I am stuck and need help." Train the agent to recognize when it is repeating itself or failing to make progress, and explicitly call stuck_help_tool instead of continuing to iterate. 

The tool terminates the agent loop, logs the failure with full context, and optionally triggers an alert for human review.​

This inverts the normal loop prevention logic. Instead of the system detecting loops externally, the agent self-reports when it detects its own loop. 

Combine this with external loop detection for defense in depth. If the agent correctly identifies its stuck state, it calls the tool and exits gracefully. If the agent fails to recognize the stuck state, external guardrails catch it anyway.

Getting the agent to reliably call the stuck tool requires careful prompt engineering and potentially fine-tuning. The system prompt needs to explicitly describe loop patterns the agent might encounter and instruct it to call stuck_help_tool when those patterns emerge. 

Example: "If you have called the same tool with similar parameters more than 3 times without getting new information, call stuck_help_tool immediately instead of continuing."

Nobody Mentions it Until Your Bill Arrives

That $47,000 loop disaster mentioned earlier happened because the team did not implement cost monitoring or automatic shutoffs. 

Eleven days of uncontrolled agent execution adds up fast, especially when tool calls and token usage multiply through loops.​

Complex agents using tool-calling patterns can consume 5-20x more tokens than simple LLM chains precisely because of loops and retries. 

Each iteration through a ReAct loop costs tokens: reasoning tokens to decide what to do, tool call tokens, tool result tokens fed back to the LLM, and additional reasoning tokens to process the results. Multiply that by 20 iterations instead of 3, and your token costs increase by nearly an order of magnitude.​

Tool calls often dump thousands of tokens of context that the agent never uses. When you call an API that returns a large JSON object but the agent only needs one field, you still pay tokens to process the entire object. 

When your agent calls a search API that returns 10 results with full text, but the agent only reads the first 2, you paid for all 10. Loops amplify this waste. Twenty iterations of unused context costs add up to real money.​

Production-grade agent systems implement per-session cost tracking. Before allowing an agent to call another tool, check the session's accumulated cost. 

If it exceeds a threshold (say, $5 for a free-tier user or $50 for a paid user), block additional tool calls and force termination. Return whatever partial results exist along with a message explaining the cost limit was reached.

This protects both you and your users. You avoid situations where a single malicious or accidentally malformed request racks up thousands in API fees. 

Users avoid surprise bills from accidentally triggering expensive agent behaviors. The cost limits should be configurable by user tier, task type, and organizational settings.

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Observability: Seeing Inside the Black Box

You cannot fix loops you cannot see. Agent observability goes beyond traditional logging. You need traces that capture prompts, tool calls, model outputs, reasoning steps, and decision points in real-time. When a loop occurs, traces let you reconstruct exactly what the agent was thinking and why it kept iterating.​

OpenTelemetry integration provides standardized tracing for agent systems. Each agent session becomes a trace with spans for reasoning steps, tool calls, and model invocations. 

When you examine a loop, you see the full span timeline: Thought 1 → Tool Call A → Thought 2 → Tool Call A (duplicate) → Thought 3 → Tool Call A (duplicate again). The pattern jumps out visually in trace visualization tools.

Monitoring for infinite loops requires detecting patterns like repeated tool calls with no variation. Observability platforms can alert when an agent calls the same tool more than N times in one session, when token usage spikes beyond normal ranges, or when session duration exceeds expected bounds. These alerts catch loops in progress, allowing you to kill the session before costs spiral.​

LangSmith and similar debugging tools provide web interfaces to visualize agent runs in detail. You see the exact inputs and outputs of every step, making it easy to spot where the agent went off track. 

When a user reports "the agent gave me a weird answer," you can pull up that specific session's trace and see exactly what tools the agent called, what results it received, and what reasoning it applied.​

Good observability also tracks success metrics: what percentage of agent sessions complete successfully, what percentage hit max iterations, what percentage get terminated by resource limits, and what the median token cost per successful session looks like. 

These metrics guide tuning. If 30% of sessions hit max iterations, you need to increase limits or fix the underlying workflow. If median token cost is rising over time, investigate whether loops are becoming more common or tool results are getting larger.

Implementing This Without Losing Your Mind

Theory is nice. Implementation determines whether your agent actually works or joins the $47K failure club. Here are patterns that work in production without requiring a PhD in agent orchestration.

Start With Conservative Limits

Set strict guardrails first, then relax them based on real usage data. Begin with max iterations of 5, token limit of 10,000 per session, and 60-second timeout. 

Most tasks should complete within these bounds. When legitimate tasks hit limits, examine why and adjust accordingly. This approach prevents catastrophic failures while you learn your agent's actual resource requirements.

Conservative limits also force you to design efficient workflows. If your agent needs 20 iterations to answer simple questions, the problem lies in your workflow design, not your iteration limits. Fix the workflow instead of just raising limits.

Log Everything, But Smart

Logging every token is expensive and creates massive log volumes. Instead, log strategically: always log tool calls with parameters and results, always log iteration counts and termination reasons, sample 10% of reasoning steps for quality analysis. 

When sessions fail or hit limits, bump logging to 100% for that session to capture debugging context.

Structure logs for searchability. Tag each log entry with session ID, user ID, task type, and agent version. When investigating issues, you can quickly filter to relevant sessions. When tracking down a bug reported by a specific user, pull their last 10 agent sessions and analyze patterns.

Build a Manual Override

Sometimes loops happen for weird reasons you did not anticipate. Implement an admin interface where you can view active agent sessions and manually terminate them. When you see a session that has been running for 10 minutes on a task that should take 30 seconds, kill it. 

When you see a user accidentally triggering expensive research tasks repeatedly, pause their agent access until you investigate.​

The manual override serves as a last-resort circuit breaker. Your automatic guardrails should catch 99% of loops, but the 1% that slip through need a human kill switch.

Test With Adversarial Prompts

Before deploying, deliberately try to break your agent. Send prompts designed to trigger loops: "Keep searching until you find the perfect answer" or "Research this topic thoroughly by exploring every possible angle." See if your guardrails catch the loops. 

See if your agent correctly recognizes impossible tasks and terminates gracefully instead of iterating forever.

Adversarial testing reveals gaps in your guardrails. You might discover that agents interpret "thoroughly" as "call every tool 50 times." 

You might find that certain phrasings confuse the termination logic. Finding these issues in testing costs free-tier tokens and time. Finding them in production costs money and user trust.

Version Your System Prompts

When you modify system prompts or tool descriptions to fix loop issues, version the changes and track which version each agent session used. 

This makes debugging trivial. When loops suddenly increase after a prompt update, you know exactly what changed and can roll back or iterate.

Versioning also enables A/B testing. Run 50% of traffic on the old prompt, 50% on the new prompt, and compare loop rates, success rates, and token costs. If the new prompt reduces loops by 30% but increases failures by 40%, you know it needs more work.

The Cheap, Practical, Actually-Works-On-Tuesdays Solutions

You do not need enterprise observability platforms or custom-built agent frameworks to implement loop prevention. Here are solutions that work at bootstrap and scale.

Use Framework Built-Ins First

LangChain, LangGraph, Semantic Kernel, and similar frameworks include built-in loop prevention mechanisms. LangChain's AgentExecutor has max_iterations parameter. 

LangGraph lets you define explicit max_concurrency and max_iterations in graph nodes. Use these before building custom solutions.​

The built-ins handle common cases adequately. They work out of the box, require minimal configuration, and integrate with the framework's debugging tools. 

Custom solutions make sense when you have specific requirements the built-ins do not address, but start simple.

Repetition Detection in 10 Lines

Implement basic repetition detection without fancy libraries. Store the last 3 actions in a list. Before each new action, compute string similarity between the new action and each stored action. If similarity > 0.9 for any stored action, block the new action. This catches obvious loops with minimal code.

String similarity can use simple metrics like Levenshtein distance or fancier embeddings-based approaches depending on your needs and budget. For most use cases, Levenshtein works fine. You are looking for nearly identical actions, not semantic similarity.

Cloud Function Timeouts

Deploy your agent as a cloud function (AWS Lambda, Google Cloud Functions, etc.) with strict timeout limits. The cloud provider automatically kills functions that run longer than the timeout. Set the timeout to 2-3x your expected task duration. This provides automatic loop protection at the infrastructure level.

The downside: abrupt termination loses context and makes debugging harder. Combine this with graceful termination logic that tries to save state before timeout. Catch the timeout signal, save whatever partial results exist, and return an error indicating timeout.

Simple Cost Tracking

Before each LLM call, calculate token cost based on your pricing tier and accumulate it in a session variable. Before each tool call, add the estimated tool cost. Compare accumulated cost to session limit. If exceeded, terminate. This does not require fancy tracking infrastructure, just basic arithmetic.

Cost tracking can be approximate. You do not need to calculate exact token counts before the call. Estimate based on prompt length and expected response length. 

Errors of 10-20% do not matter for cost limiting. You want order-of-magnitude protection, not penny-perfect accounting.

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What Production Actually Looks Like

Teams running agents at scale report similar patterns. Initial deployment works fine with curated test cases. Production traffic includes edge cases, adversarial users, and plain weird requests that nobody anticipated. Loops emerge from the gaps between "what we tested" and "what users actually do."

Scaling requires shifting from reactive loop fixes to proactive loop prevention. Instead of waiting for a $47K bill, implement monitoring that alerts when sessions exceed normal bounds. 

Instead of manually investigating each loop, build automated analysis that clusters loop patterns and suggests fixes. Instead of adjusting limits session-by-session, use historical data to set dynamic limits based on task type and user behavior.

One production pattern that works: tiered agent execution. Simple requests get a fast, limited agent with strict guardrails. 

Complex requests get a more capable agent with higher limits and more tools. The routing happens before agent execution based on request classification. This prevents using expensive, loop-prone agents for simple tasks while ensuring complex tasks have the resources they need.

Another pattern: progressive timeout. First iteration has a 5-second timeout. Second iteration has a 7-second timeout. Third iteration has a 10-second timeout. Each subsequent iteration gets slightly more time, but the total session timeout remains fixed. 

This allows legitimate multi-step tasks to complete while preventing runaway loops from consuming unlimited time.

Cost controls become more sophisticated at scale. Instead of flat per-session limits, implement rate-based limits: users can spend up to $X per hour or $Y per day on agent tasks. This allows power users to make heavy use without letting any single session destroy the budget. 

Combine with per-session caps to prevent individual runaway sessions even within the rate limit.

When Loops Reveal Deeper Problems

Sometimes persistent loops indicate that your task is too complex for a single agent, your tools are poorly designed, or your workflow makes assumptions that do not hold. An agent that consistently loops when trying to complete a specific type of task is telling you something.

Redesign the workflow to break complex tasks into smaller, sequential steps with clear completion criteria for each step. 

Replace tools that return ambiguous results with tools that return structured, easily validated outputs. Refine prompts to set clearer expectations about what "complete" looks like for each task type.

Loops can also reveal training data issues. If your agent loops specifically on requests similar to examples in its training data where the task required extensive iteration, it learned to iterate extensively. 

Prompt engineering or fine-tuning can correct this behavior by providing examples of concise, efficient task completion.

The hardest realization: some tasks genuinely require many iterations, and capping them breaks functionality. Research tasks, creative tasks, and tasks involving uncertain information sometimes need 20-30 iterations to complete properly. 

For these cases, implement progress tracking. Require the agent to report measurable progress at each iteration. If iterations 5-10 made no progress (same tools called, similar results obtained, no new information gathered), terminate. If iterations 5-10 each added new information, allow continuation.

Conclusion: Escape Hatches Are Not Optional

Building AI agents without loop prevention resembles driving without brakes. Sure, the car goes forward nicely when the road is clear. But when reality arrives, you have no way to stop before the crash. 

Start with framework built-ins, add repetition detection, implement resource limits, build observability, and test adversarially. These steps take a few hours and prevent most loop disasters. 

Then iterate based on production data. Watch which tasks hit limits, where loops emerge, and what costs accumulate. Adjust your guardrails to match real usage patterns instead of theoretical concerns.

The lazy approach that actually works: conservative defaults with monitoring and gradual relaxation. Set strict limits, deploy, watch what breaks, fix the workflow or relax the limits appropriately. 

This beats trying to design perfect limits upfront (impossible) or deploying with no limits and hoping for the best (expensive).

Your agent will loop. Design for when, not if. Build escape hatches before your first production deployment, not after your first unexplained API bill. 

The hour you spend implementing basic guardrails today prevents the day you spend explaining to management why a chatbot cost more than your salary.

I hope this guide gives you the tools to design agent workflows that complete tasks efficiently instead of burning through your budget on infinite reasoning spirals. Come back later for more posts on building AI systems that work in production, not just in demos.