Constructing an AI Agent to Detect and Deal with Anomalies in Time-Sequence Information

As a knowledge scientist engaged on time-series forecasting, I’ve run into anomalies and outliers greater than I can rely. Throughout demand forecasting, finance, site visitors, and gross sales information, I maintain operating into spikes and dips which are exhausting to interpret.

Anomaly dealing with is normally a grey space, not often black or white, however indicators of deeper points. Some anomalies are actual alerts like holidays, climate occasions, promotions, or viral moments; others are simply information glitches, however each look the identical at first look. The sooner we detect anomalies in information, the sooner motion might be taken to stop poor efficiency and injury.

We’re coping with vital time-series information, and detecting anomalies is essential. In case you take away a real occasion, a priceless sign information level is eliminated, and should you maintain a false alarm sign, the coaching information incorporates noise.

Most ML-based detectors flag spikes primarily based on Z-scores, IQR thresholds, or different static strategies with none context. With latest developments in AI, we’ve got a greater choice to design an anomaly-handling agent that causes about every case. An agent that detects uncommon conduct, checks context, and decides whether or not to repair the info, maintain it as an actual sign, or flag it for assessment.

On this article, we construct such an agent step-by-step that mixes easy statistical detection with an AI agent that acts as a primary line of protection for time-series information, decreasing guide intervention whereas preserving the alerts that matter most. We are going to detect and deal with anomalies in COVID-19 information by autonomous decision-making primarily based on the severity of the anomaly, utilizing:

1. Reside epidemiological information from the illness.sh API.
2. Statistical anomaly detection.
3. Severity classification.
4. A GroqCloud-powered AI agent that takes autonomous choices whether or not to:
   • Repair the anomaly
   • Hold the anomaly
   • Flag anomaly for human assessment

That is agentic choice intelligence, not merely anomaly detection.

Why is conventional anomaly detection alone not sufficient?

There are conventional ML strategies like isolation forests designed for anomaly detection, however they lack end-to-end choice orchestration. They’re unable to behave on them rapidly sufficient in manufacturing environments. We’re implementing an AI agent to fill this hole by turning uncooked anomaly scores into autonomous, end-to-end choices dynamically on reside information.

Conventional Anomaly Detection

The normal anomaly detection follows the pipeline method as drawn under:

Limitations of Conventional Anomaly Detection

• Works on static guidelines and manually units thresholds.
• It’s single-dimensional and handles easy information.
• No contextual reasoning.
• Human-driven choice making.
• Handbook-driven motion.

Anomaly Detection and Dealing with with an AI Agent 

The AI Agent anomaly detection follows the pipeline method as drawn under:

Why does this work higher in observe?

• Works on real-time information.
• It’s multidimensional and may deal with complicated information.
• Works on contextual reasoning.
• Adaptive & self-learning choice making.
• Take autonomous motion.

Selecting a sensible dataset for our instance

We’re utilizing real-world COVID-19 information to detect anomalies, as it’s noisy, reveals spikes, and the outcomes assist in the advance of public well being.

What do we would like the AI Agent to resolve?

The aim is to repeatedly monitor COVID-19 information, discover anomalies, outline their severity, and take autonomous choices and resolve motion to be taken:

• Flag anomaly for human assessment
• Repair the anomaly
• Hold the anomaly

Information Supply

For the info, we’re utilizing free, reside illness.sh information through API. This API supplies information on every day confirmed instances, deaths and recoveries. For the AI Agent implementation, we’re specializing in every day case counts, which are perfect for anomaly detection.

Information license: This tutorial makes use of COVID-19 historic case counts retrieved through the illness.sh API. The underlying dataset (JHU CSSE COVID-19 Information Repository) is licensed underneath CC BY 4.0, which allows business use with attribution. (Accessed on January 22, 2026)

How do the items match collectively?

Excessive-Stage system structure of the anomaly detection on COVID-19 information utilizing an AI Agent is as follows:

Constructing the AI Agent Step-by-Step 

Let’s go step-by-step to grasp load information utilizing illness.sh, detect anomalies, classify them, and implement an AI agent that causes and takes acceptable motion as per the severity of the anomalies.

Step 1: Set up Required Libraries

Step one is to put in required libraries like phidata, groq, python-dotenv, tabulate, and streamlit.

pip set up phidata
pip set up groq
pip set up python-dotenv #library to load .env file
pip set up tabulate
pip set up streamlit

Step 2: Setting File Set-up

Open your IDE and create a challenge folder, and underneath that folder, create an environmental file “.env” to retailer GROQ_API_KEY.

GROQ_API_KEY="your_groq_api_key_here"

Step 3: Information Ingestion

Earlier than constructing any agent, we’d like a knowledge supply that’s noisy sufficient to floor actual anomalies, however structured sufficient to purpose about. COVID-19 every day case counts are match as they comprise reporting delays, sudden spikes, and regime modifications. For simplicity, we intentionally prohibit ourselves to a single univariate time sequence.

Load information from the illness.sh utilizing request URL and extract the date and every day case rely primarily based on the chosen nation and the variety of days for which you wish to extract information. The information is transformed right into a structured dataframe by parsing json, formatting date and sorting chronologically.

# ---------------------------------------
# DATA INGESTION (illness.sh)
# ---------------------------------------

def load_live_covid_data(nation: str , days:int):
    url = f"https://illness.sh/v3/covid-19/historic/{nation}?lastdays={days}"
    response = requests.get(url)
    information = response.json()["timeline"]["cases"]

    df = (
        pd.DataFrame(record(information.gadgets()), columns=["Date", "Cases"])
        .assign(Date=lambda d: pd.to_datetime(d["Date"], format="%m/%d/%y"))
        .sort_values("Date")
        .reset_index(drop=True)
    )
    return df

Step 4: Anomalies Detection

We are going to now detect irregular conduct in COVID-19 time-series information by detecting sudden spikes and fast development developments. Case counts are usually steady, and enormous deviations or sharp will increase point out significant anomalies. We are going to now detect anomalies utilizing statistical strategies and binary labeling for deterministic and reproducible anomaly detection. Two parameters are calculated to detect anomalies.

1. Spike Detection
   • A sudden spike in information is detected utilizing the Z-score; if any information level falls exterior the Z-score vary, it have to be an anomaly.
2. Development Price Detection
   • The day-over-day development fee is calculated; if it exceeds 40%, it’s flagged.

# ---------------------------------------
# ANOMALY DETECTION
# ---------------------------------------
def detect_anomalies(df):
   values = df["Cases"].values
   imply, std = values.imply(), values.std()

   spike_idx = [
       i for i, v in enumerate(values)
       if abs(v - mean) > 3 * std
   ]

   development = np.diff(values) / np.most(values[:-1], 1)
   growth_idx = [i + 1 for i, g in enumerate(growth) if g > 0.4]

   anomalies = set(spike_idx + growth_idx)
   df["Anomaly"] = ["YES" if i in anomalies else "NO" for i in range(len(df))]

   return df

If there may be an anomaly in keeping with both spike or development or with each parameters, the “Anomaly” is ready to “YES”; in any other case set to “NO”.

Step 5: Severity Classification

All anomalies usually are not equal; we’ll classify them as ‘CRITICAL’, ‘WARNING’, or ‘MINOR’ to information AI Agent choices. Fastened rolling home windows and rule-based thresholds are used to categorise severity. Severity is classed solely when an anomaly exists; in any other case, Severity, Agent Determination, and Motion parameters within the dataframe are set to ‘clean’.

# ---------------------------------------
# CONFIG
# ---------------------------------------
ROLLING_WINDOW = 7
MIN_ABS_INCREASE = 500

# ---------------------------------------
# SEVERITY CLASSIFICATION
# ---------------------------------------
def compute_severity(df):
    df = df.sort_values("Date").reset_index(drop=True)
    df["Severity"] = ""
    df["Agent Decision"] = ""
    df["Action"] = ""
    for i in vary(len(df)):
        if df.loc[i, "Anomaly"] == "YES":
            if i < ROLLING_WINDOW:
                df.loc[i, "Severity"] = ""

            curr = df.loc[i, "Cases"]
            baseline = df.loc[i - ROLLING_WINDOW:i- 1, "Cases"].imply()

            abs_inc = curr - baseline
            development = abs_inc / max(baseline, 1)

            if abs_inc < MIN_ABS_INCREASE:
                df.loc[i, "Severity"] = ""
            if development >= 1.0:
                df.loc[i, "Severity"] = "CRITICAL"
            elif development >= 0.4:
                df.loc[i, "Severity"] = "WARNING"
            else:
                df.loc[i, "Severity"] = "MINOR"
    return df

Within the above code, to categorise the anomaly severity, every anomaly is in contrast with 7-day historic information (ROLLING_WINDOW = 7), and absolute and relative development are calculated.

1. Absolute Development

A MIN_ABS_INCREASE = 500 is outlined as a config parameter the place modifications under this worth are thought of very small, a negligible change. If absolutely the development is lower than MIN_ABS_INCREASE, then ignore it and maintain the severity clean. Absolute development detects significant real-world affect, doesn’t react to noise or minor fluctuations, and prevents false alarms when development proportion is excessive.

2. Relative Development:

Relative development helps in detecting explosive developments. If development is larger than or equal to 100% improve over baseline, it means a sudden outbreak, and it’s assigned as ‘CRITICAL’; if development is larger than 40%, it means sustained acceleration and wishes monitoring, and it’s assigned as ‘WARNING’; in any other case assigned as ‘MINOR’. 

After severity classification, it’s prepared for the AI Agent to make an autonomous choice and motion.

Step 6: Construct Immediate for AI Agent

Under is the immediate that defines how the AI agent causes and makes choices primarily based on structured context and predefined severity when an anomaly is detected.  The agent is restricted to 3 specific actions and should return a single, deterministic response for protected automation.

def build_agent_prompt(obs):
    return f"""
You're an AI monitoring agent for COVID-19 information.

Noticed anomaly:
Date: {obs['date']}
Instances: {obs['cases']}
Severity: {obs['severity']}

Determination guidelines:
- FIX_ANOMALY: noise, reporting fluctuation
- KEEP_ANOMALY: actual outbreak sign
- FLAG_FOR_REVIEW: extreme or ambiguous anomaly

Reply with ONLY one in every of:
FIX_ANOMALY
KEEP_ANOMALY
FLAG_FOR_REVIEW
"""

Three information factors, i.e., date, variety of instances reported, and severity, are supplied to the immediate explicitly, which helps the AI Agent to decide autonomously.

Step 7: Create your Agent with GroqCloud

We are actually creating an autonomous AI agent utilizing GroqCloud that makes clever contextual choices on detected anomalies and their severities and takes acceptable actions. Three predefined actions for the AI Agent implement validated outputs solely.

# ---------------------------------------
# BUILDING AI AGENT
# ---------------------------------------
agent = Agent(
    title="CovidAnomalyAgent",
    mannequin=Groq(id="openai/gpt-oss-120b"),
    directions="""
You're an AI agent monitoring reside COVID-19 time-series information.
Detect anomalies, resolve in keeping with the anomaly:
"FIX_ANOMALY", "KEEP_ANOMALY", "FLAG_FOR_REVIEW"."""
)
for i in vary(len(df)):
    if df.loc[i, "Anomaly"] == "YES":
        obs = build_observation(df, i)
        immediate = build_agent_prompt(obs)
        response = agent.run(immediate)

        choice = response.messages[-1].content material.strip()
        choice = choice if choice in VALID_ACTIONS else "FLAG_FOR_REVIEW"
        df = agent_action(df, i, choice)

An AI agent named “CovidAnomalyAgent” is created, which makes use of an LLM mannequin hosted by GroqCloud for quick and low-latency reasoning. AI Agent runs a well-defined immediate, observes information, contextual reasoning, makes an autonomous choice, and takes actions inside protected constraints.

An AI Agent just isn’t dealing with anomalies however making clever choices for every detected anomaly. The agent’s choice precisely displays anomaly severity and required motion.

# ---------------------------------------
# Agent ACTION DECIDER
# ---------------------------------------
def agent_action(df, idx,motion):
    df.loc[idx, "Agent Decision"] = motion

    if motion == "FIX_ANOMALY":
        fix_anomaly(df, idx)

    elif motion == "KEEP_ANOMALY":
        df.loc[idx, "Action"] = "Accepted as an actual outbreak sign"

    elif motion == "FLAG_FOR_REVIEW":
        df.loc[idx, "Action"] = "Flagged for human assessment"
    return df

AI Agent ignores regular information factors with no anomaly and considers solely information factors with “ANOMALY= YES”. The AI agent is constrained to return solely three legitimate choices: “FIX_ANOMALY“, “KEEP_ANOMALY“, and “FLAG_FOR_REVIEW“, and accordingly, motion is taken as outlined within the desk under:

For minor anomalies, the AI agent mechanically fixes the info, preserves legitimate anomalies as-is, and flags vital instances for human assessment.

Step 8: Repair Anomaly

Minor anomalies are attributable to reporting noise and are corrected utilizing native rolling imply smoothing over latest historic values.

# ---------------------------------------
# FIX ANOMALY
# ---------------------------------------

def fix_anomaly(df, idx):
    window = df.loc[max(0, idx - 3):idx - 1, "Cases"]
    if len(window) > 0:
        df.loc[idx, "Cases"] = int(window.imply())

    df.loc[idx, "Severity"] = ""
    df.loc[idx, "Action"] = "Auto-corrected by an AI agent"

It takes the rapid 3 days of previous information, calculates its imply, and smooths the anomaly by changing its worth with this common. By the native rolling imply smoothing method, non permanent spikes and information glitches might be dealt with. 

As soon as an anomaly is mounted, the info level is now not thought of dangerous, and severity is deliberately eliminated to keep away from confusion. “Motion” is up to date to “Auto-corrected by an AI agent”.

Full Code

Kindly undergo the whole code for the statistical anomaly detection and AI Agent implementation for anomaly dealing with.

https://github.com/rautmadhura4/anomaly_detection_agent/tree/most important

Outcomes

Let’s evaluate the outcomes for the nation, “India,” with various kinds of severity detected and the way the AI Agent handles them.

Situation 1: A Native Implementation

The primary try is a local implementation the place we detect minor anomalies and the AI Agent fixes them mechanically. Under is the snapshot of the COVID information desk of India with severity.

We now have additionally applied a Streamlit dashboard to assessment the AI Agent’s choices and actions. Within the under end result snapshot, you’ll be able to see that varied minor anomalies are mounted by the AI Agent.

This works greatest when anomalies are localized noise slightly than regime modifications.

Situation 2: A Boundary Situation

Right here, vital anomalies are detected, and the AI Agent raises a flag for assessment as proven within the snapshot of the COVID information desk of India with severity.

On the Streamlit dashboard AI Agent’s choices and actions are proven within the end result snapshot. You’ll be able to see that every one the vital anomalies had been flagged for human assessment by the AI Agent.

Severity gating prevents damaging auto-corrections in high-impact anomalies.

Situation 3: A Limitation 

For the limitation situation, warning and demanding anomalies are detected as proven within the snapshot of the COVID information desk of India with severity.

On the Streamlit dashboard AI Agent’s choices and actions are proven under within the end result snapshot. You’ll be able to see that the vital anomaly is flagged for human assessment by AI Agent, however the WARNING anomaly is mechanically mounted. In lots of actual settings, a WARNING-level anomaly ought to be preserved and monitored slightly than corrected.

This failure highlights why WARNING thresholds ought to be tuned and why human assessment stays important.

Use the whole code and check out anomaly detection for the COVID-19 dataset, with totally different parameters.

Future Scope and Enhancements

We now have used a really restricted dataset and applied rule-based anomaly detection, however sooner or later, some enhancements might be performed within the AI Agent implementation:

1. In our implementation, an anomaly is detected, and a choice is made primarily based on case rely solely. Sooner or later, information might be extra elaborate with options like hospitalization information, vaccination information, and others.

2. Anomaly detection is finished right here utilizing statistical strategies, which may also be ML-driven sooner or later to establish extra complicated patterns.

3. Now, we’ve got applied a single-agent structure; sooner or later multi-agent structure might be applied to enhance scalability, readability, and resilience.
4. Sooner or later human suggestions loop also needs to take care to make improved choices.

Ultimate Takeaways

Smarter AI brokers allow operational AI that makes choices utilizing contextual reasoning, takes motion to repair anomalies, and escalates to people when wanted. There are some sensible takeaways to remember whereas constructing an AI Agent for anomaly detection:

• To detect anomalies, use statistical strategies and implement AI brokers for contextual decision-making.

• Minor anomalies are protected to be autocorrected as they’re usually reported as noise. Crucial ought to by no means be autocorrected and flagged for assessment by area consultants in order that real-world alerts don’t get suppressed.

• This AI agent should not be utilized in conditions the place anomalies immediately set off irreversible actions.

When statistical strategies and an AI agent method are mixed correctly, they rework anomaly detection from simply an alerting system right into a managed, decision-driven system with out compromising security.