Building Resilient AI Agents

Written By Rudresh Trivedi

Most AI agents today are like a glass cannon. They are versatile and intelligent in executing and navigating tasks, yet shatter at the smallest unexpected error. When they work, they offload a huge amount of repetitive effort. But in production, most agents remain brittle: bounded by narrow context windows, unpredictable outputs, and fragile execution flows. A single unhandled exception can terminate a multi-minute workflow, wasting compute, time, and money. To build agents that run for days, not minutes, we need to stop equating intelligence with reliability. True resilience comes from architecture, not clever prompts.

Every resilient system shares three foundations:

  • Validation — Every output must be structurally and semantically verified before the next step executes.

  • Execution — Every agent must follow a deterministic plan, a graph that defines actions and dependencies.

  • Transactional — Every operation must be atomic, deterministic: it either completes, retries, or rolls back safely.

These three principles form the backbone of how LatentSpin builds long-running, self-healing agents. In the sections that follow, we’ll explore how these principles solve three core engineering challenges. limited context windows, unexpected errors, and state management for long-running decision flows. To make this tangible, we’ll build a simple websearch + citation agent using resilient design patterns.

Validation - Pitfall of Bounded Context

Consider a common task: webpage summarization. A naive approach might be a simple chain of execution steps: extract data then feed it to an LLM  for summarization. This sounds simple, but the devil is in the details. Let's take a look at a small wikipedia page and its rendered html content (using https://github.com/unclecode/crawl4ai ).

(Disclaimer: following LLM invocations are done in gpt-oss:20b)

url = "https://en.wikipedia.org/wiki/Robustness"
data = await crawler.arun(url=url)
invoke_response = LLM_Invoke("From this rendered html give me key information", data)
print(f"Webpage summary length: len(data.html)")
print(f"Webpage summary: {invoke_response}")

### Output
Webpage summary length: 173719
Webpage summary: "<mw-parser-output><p>Robustness may refer to <!-- mw-headline --> Robustness (engineering)? system..."

This wikipedia page has ~2.5K useful characters but rendered html for this small and simple page is noisy, filled with styling, scripts and layout wrappers. This highlights the hard limits on what content we can feed into an LLM’s context. Extracting signal from rendered HTML remains a major challenge in the agentic ecosystem — it’s messy, token-expensive, and often hides key information within noise. Recent work (see Shi et al., 2025) explores language models driven extraction frameworks to address exactly this.

Many smaller language models have 32K–256K context windows and will struggle to produce accurate results for this use case. Frontier models, with 500K–1M token contexts, can easily handle this example and generate a reasonable response. However, this approach doesn’t scale. Real webpages are far larger than a simple Wikipedia article, and as the workflow grows in scope, it risks token clipping, degraded summarization quality, and increased hallucinations during deeper research.

A practical solution is to apply a refined loop pattern that iteratively summarizes data in manageable segments. This keeps the effective context window tight while preserving semantic continuity between chunks. In practice, CHUNK_SIZE is chosen to be a safe fraction of the model’s maximum context length, ensuring that no iteration overflows the token limit and that each summary can cleanly feed into the next step.

Below is a simplified pseudo-code example demonstrating this approach.

# Disclaimer: This is simplified pseudocode to illustrate the concept.
# A real-world system would combine this pattern with HTML distillation and 
# specialized extractors for higher efficiency. here are links to dive deep  into html extractors and distillation steps
# https://arxiv.org/html/2503.01151v1
# https://aws.amazon.com/blogs/machine-learning/an-introduction-to-preparing-your-own-dataset-for-llm-training/
# https://arxiv.org/pdf/2407.15021

html_content_data = extract_text_content_from_rendered_html(data)
for chunk in html_content_data.split(CHUNK_SIZE):
  prompt = f" prompt to extract informaiton from html ... {chunk}, old summary: {summary} ... "
  summary = LLM_Invoke(prompt)

invoke_response = LLM_Invoke("From this rendered html give me key information", data)
print(f"Webpage summary length: {len(summary)}")
print(f"Webpage summary: {invoke_response}")

### Output
# Webpage summary length: 829
# Webpage summary: "Robustness is the ability of a system to maintain performance despite uncertainties ... "

This iterative summarization distills high volume data into compact information-rich segments for the context. It’s efficient, resilient to input noise, and helps to ensure that the final summary fits in the model's processing capacity.

This example highlights how even large context windows collapse under real-world workloads. Any long-running or iterative task will eventually exceed the model’s memory budget, regardless of summarization or chunking strategy. Addressing that limitation is central to our work at LatentSpin, where we focus on models that continuously absorb and retain context as they operate.

Execution - Pitfall of Chained Steps

After web page summarization, to continue the web search + citation agent we will need to format the data into json to access the data reliably. Even though frontier LLMs have a small error rate for individual tasks, those errors compound during chained execution as an agent runs for minutes/hours.

Consider this example: supposing an agent asks a LLM to format our data into JSON and the LLM is reliable over 99.99% of the time, over thousands of execution calls this 0.01% error rate will inevitably lead to breakdown. The "99.99% failure" model is not a mere hypothetical; it is a well-documented phenomenon in multi-step agentic systems. Research provides a formal name for this problem: "compounding errors" (see papers by AWS and Microsoft Research Group).

schema = {"name": str, "summary": str}
success_count = 0
failure_count = 0

for step in range(1000):
    response = LLM_Invoke("Extract info as JSON", input=data[step])
    
    if validate_json(response, schema):
        success_count += 1
    else:
        failure_count += 1

print(f"Total successful steps: {success_count}")
print(f"Total failed steps: {failure_count}")

### Output
# Total successful steps: 997
# Total failed steps: 3

This pattern eventually fails because of one simple malformed object generation.

Now imagine a LLM making a decision for a more complex task and choosing the next action. A small drift in chained execution can lead to contradictory outputs or execution failure causing minutes / hours of wasteful time and cost. Without automatic validation, recovering from these mistakes can propagate to unstable agents.

The fix is architectural: retries, validation and real-time learning should be key components of an Agentic Framework. LLM calls should be instructed with proper parameters for output schema (supported by all modern LLM APIs) and any LLM API should be wrapped with output schema validation and LLM judges for semantic correctness with a way to learn from mistakes / hallucinations.

To address the weaknesses of the previous example, we can make each model call type-safe and add structured validation with automatic retries. This ensures that transient or malformed outputs don’t silently propagate downstream. By wrapping the core call in a guarded robust_invoke function, the system can self-correct, retrying until a valid JSON response is produced.

def robust_invoke(prompt, schema):
    for attempt in range(MAX_RETRIES):
        result = LLM_Invoke(prompt, output_schema=schema)
        if validate_json(result, schema) and LLM_Judge("Is this a good answer?", result):
            return result
    raise RuntimeError("LLM repeatedly produced invalid output")

for step in range(1000):
    if validate_json(robust_invoke(prompt, schema), schema):
        success_count += 1
    else:
        failure_count += 1

print(f"Total successful steps: {success_count}")
print(f"Total failed steps: {failure_count}")

### Output
# Total successful steps: 1000
# Total failed steps: 0

Transactional - Pitfall of State Management

Continuing our websearch + citation agent builder, the next logical step is to perform a Google search, extract the top URLs, then scrape each page to gather information that can be synthesized into a citation-rich response. 

A naive approach might look like this: scrape Google search page, distill it to collect top URLs and then scrape collected URLs to gather information about the user query to generate final summarized output.

user_query = "how to build resilient agents?"
html = fetch_rendered_html(url=f"https://google.com/search?q={user_query}") # Using crawl4ai to get rendered html
urls = distill(data=html, prompt=f"Collect top 5 search URLs from google search page") # Using distiller from above example to extract key information
results = []

for link in urls[:5]:
    html = fetch_rendered_html(link)
    summary = LLM_Invoke("Summarize this page", html)
    results.append({"url": link, "summary": summary})

final_summarized_output = LLM_Invoke(f"Based on all this summaries generate an answer with citation for this query {search} summaries: {results}")

At first glance, this approach seems reasonable and every component works fine in isolation. The URL extractor succeeds, each webpage is summarized, and the results are collected.

But as soon as this workflow runs in the real world, cracks appear. Some URLs are paywall traps, others lead to sponsored content or redirects, and our summarizer has no idea it’s being misled. Each individual step is “correct” ; it successfully extracted, summarized, and stored its piece but the composition is wrong. The final citation-rich response becomes skewed or low-quality because the agent has no mechanism to rollback, blacklist invalid URLs, or re-plan its search once it encounters untrustworthy pages.

Without transactional control, the workflow keeps moving forward, compounding bad data and wasting computation.

In a production-ready agent, these kinds of silent logical failures are unacceptable. We need state management that can checkpoint every step, detect when an upstream assumption (like a valid URL) fails downstream, and rollback to a clean state to retry or fetch new inputs. This is the essence of transactional execution and every operation must either complete successfully or revert safely.

This limitation isn’t unique to our example — it’s a systemic issue in today’s agent frameworks. Recent research (see Darouiche et al., 2025) reviewing CrewAI, LangGraph, AutoGen, and MetaGPT highlights critical gaps in composability, generalizability, state persistence, and service-level orchestration. Without standardized guardrails, robust memory, and interoperable communication protocols, deploying resilient, production-grade agents is extremely difficult. Even with good context window management and retries, agents need a rigid execution plan that can detect errors and rollback when needed. Otherwise, the same input can produce divergent runs, breaking reliability. Frameworks like LangChain provide graph-based DSLs, but they often evolve into bespoke, one-off agents that are hard to maintain or reproduce.

At LatentSpin, we solve this by combining a rigid execution framework with automatic task destructuring and planning. Users negotiate a “flow” with the agent and a deterministic plan of action is constructed and once the flow is locked in, the agent executes it. Context window management, retries, validations and roll backs are automatically handled inside the flow, not left to the user.

The FlowDSL framework at LatentSpin doesn’t just provide a convenient interface for chaining operations. It enforces atomic, transactional execution for each step. Under the hood, every step is tick-based: its output is stored persistently, validated against schemas or semantic checks, and can automatically retry, resume, or roll back if failures occur. This guarantees deterministic execution and prevents cascading errors across multi-step workflows. Importantly, the persisted state and validation layer allow the agent builder to learn from prior executions, continuously refining future plans and improving reliability.

Here is a simplified snippet of what FlowDSL is doing behind the scenes.
(Disclaimer: this framework illustrates a simple FlowDSL internals)

class FlowStep:
    def __init__(self, ???, schema=None, retries=3):
        self.state = None  # persistent checkpoint

    def execute(self, input_data):
	## Validation is baked in here
        ???

class Flow:
    def __init__(self):
        self.steps = []

    def via(self, step):
        self.steps.append(step)
        return self

    def run(self, input_data):
	## Roll backs is baked in here with the help of managed state and LLM judge validation
	???

By exposing the Flow interface while managing execution atomicity, persistence, retries, validation, and rollbacks internally, LatentSpin ensures that multi-step, long-running agent workflows remain reliable, fault-tolerant, and reproducible, even when interacting with unpredictable external services like web pages. This approach addresses the failures in the earlier naive example, preventing lost progress, malformed summaries, and cascading errors by checkpointing each step and making execution fully transactional with rollback capability.

Using this foundation, composing a real agent becomes ergonomic and declarative:

user_query = "how to build AI agents?"

flow = Flow()
flow.of(f"https://google.com/search?q={user_query}")

# 1. Summarize the search page to collect next URLs
# Each .via() defines a recoverable, typed step with its own checkpoint, retry policy and validation
flow = flow
  .via(GetPageContent()) # open URL and get rendered html
  .via(Distiller(input="raw html", output="list of URLs")) # use chuncking method shown above to extract useful information

# 2. Concurrently scrape each search result and 
flow = flow.via(IteratorFlow(
  subflow=Flow()
    .via(GetPageContent())
    .via(Distiller(input = "raw html", output = search))
))

# 3. Generate final output with citations
flow = flow.via(LLM_Invoke("summarize content and cite sources"))

flow.run()

### Output
#[
#  {
#    "citation": "https://en.wikipedia.org/wiki/Artificial_intelligence",
#    "response": "Artificial Intelligence (AI) agents are software entities #capable of perceiving ..."
#  },
#  {
#    "citation": "https://www.salesforce.com/agentforce/agent-builder/how-to-build/",
#    "response": "AI agents are intelligent systems designed to automate processes and perform tasks ..."
#  },
#...
#]

This architecture enforces execution planner to build a complex agent with rigid and resilient execution

Conclusion

Building agents is fun and building resiliency is key to long-term success. At LatentSpin, we build agents that work for days and not minutes. We tackle three fundamental agent building blocks engineering problems to do this with innovative solutions.

  • Context is managed via continuous learning which can capture real world changes.

  • Automatic retries + decision validation is baked directly into the graph via the planner.

  • The power of LLMs in distributed execution framework leverages agents to execute in deterministic flow

By solving these fundamental problems, you get resilient agents without the complexity. Without hiring expensive experts.

Rudresh Trivedi
Previous

Breaking the Monolith: Why Multi-Model Architectures Make Better Agents
Next

The Haunted Model: When Memory Comes Back to Bite