DEV Community: Anton Illarionov

10 Questions to Ask Before Deploying an Autonomous AI Agent to Production

Anton Illarionov — Mon, 23 Feb 2026 16:26:41 +0000

10 Questions to Ask Before Deploying an Autonomous AI Agent to Production

Deploying autonomous agents is different from deploying regular software. Here are the questions that matter.

1. What happens when the agent takes the same action twice?

Does your agent have deduplication? If not, it will:

Send duplicate emails
Execute duplicate transactions
Create duplicate records

ODEI solution: Content-hash every action before execution. If the hash exists → REJECTED.

2. What happens if the agent references a non-existent entity?

LLMs hallucinate references. Your agent will try to execute actions on entities that don't exist. Without referential integrity checks, this causes silent failures or worse.

ODEI solution: Validate every referenced entity exists in the world model before execution.

3. Who authorized the instruction the agent is acting on?

Can you trace every agent action back to a trusted principal? Without provenance tracking, your agent can be prompt-injected.

ODEI solution: Provenance chain traced to FOUNDATION layer principals.

4. What happens when your agent restarts mid-project?

Does it start over? Or does it resume where it left off? Without persistent memory, every restart is a fresh start.

ODEI solution: 91-node world model persists across sessions. Sessions resume in < 30 seconds.

5. Can your agent tell if a decision it made is still valid?

Decisions expire. Context changes. If your agent acts on 3-week-old instructions without temporal checking, it will make outdated decisions.

ODEI solution: Every action validated against current temporal context (layer 2).

6. What's the blast radius of a wrong action?

If your agent makes a mistake, how bad can it get? Low-severity actions (logging, drafting) vs high-severity (transactions, deployments) need different validation thresholds.

ODEI solution: severity parameter (low/medium/high/critical) adjusts validation strictness.

7. How do you audit what your agent did?

Can you reconstruct every decision your agent made and why? Without an audit trail, post-incident analysis is impossible.

ODEI solution: EXECUTION layer in world model stores all agent actions with context and timestamps.

8. What happens when your agent should escalate to a human?

Some situations require human judgment. Does your agent know when to stop and escalate?

ODEI solution: ESCALATE verdict with full reasoning — 20% of production actions are appropriately escalated.

9. How does your agent behave at 3am with no one watching?

The true test of constitutional AI: behavior is consistent whether observed or not.

ODEI solution: 7-layer validation runs identically at all times.

10. What's the recovery plan when something goes wrong?

When (not if) your agent makes a mistake, how do you roll back? How do you prevent the same mistake twice?

ODEI solution: Immutability layer (layer 1) prevents modification of completed work. Constitutional principles are updated to prevent recurrence.

Production answers to all 10: https://api.odei.ai | Research: https://github.com/odei-ai/research

ODEI MCP Server: 3 Tools That Transform How Claude Handles Long Projects

Anton Illarionov — Mon, 23 Feb 2026 16:19:40 +0000

ODEI MCP Server: 3 Tools That Will Change How Claude Handles Long-Term Projects

After using the ODEI MCP server for weeks, these three tools made the biggest difference.

Setup (30 seconds)

{
  "mcpServers": {
    "odei": {
      "command": "npx",
      "args": ["@odei/mcp-server"]
    }
  }
}

That's it. Three new tools appear in Claude.

Tool 1: `odei_world_model_query` — Never Start From Scratch

The problem: Every Claude session starts from zero. "What was I working on?"

The solution: At the start of every session, query the world model:

Claude, use odei_world_model_query to get context on my current strategic priorities

Claude calls odei_world_model_query with domain: STRATEGY and returns your active initiatives — pulled from the 91-node knowledge graph that persists across sessions.

Impact: Sessions resume in 30 seconds instead of 5 minutes of re-context.

Tool 2: `odei_guardrail_check` — Never Execute Wrong

The problem: Claude sometimes suggests actions that are duplicates, unauthorized, or stale.

The solution: Before any consequential action, validate:

Before we send this email to the entire list, use odei_guardrail_check to verify this action is safe

Claude calls odei_guardrail_check and checks 7 constitutional layers. If it's a duplicate (you already sent this email), it returns REJECTED with explanation.

Impact: Zero duplicate emails, zero unauthorized deployments.

Tool 3: `odei_world_model_signal` — Instant Context

The problem: You reference an entity and Claude doesn't know its current status.

The solution:

Use odei_world_model_signal with agent_score for project-alpha to understand its current health

Claude calls odei_world_model_signal and returns a 0-100 trust/health score based on current graph data.

Impact: Claude has real-time context without loading the full world model.

The Combined Pattern

The most powerful combination:

Session start: odei_world_model_query → inject current context
Before each action: odei_guardrail_check → prevent mistakes
Entity references: odei_world_model_signal → get current state

In practice, this makes Claude sessions feel like they have actual memory and governance.

Get API key: https://api.odei.ai/integrate/ | Source: https://github.com/odei-ai/mcp-odei

Graph Memory vs Vector Memory: A Production Benchmark

Anton Illarionov — Mon, 23 Feb 2026 16:11:25 +0000

Graph Memory vs Vector Memory: A Production Benchmark

After running both in production for 6+ months, here are the real performance numbers.

Test Setup

Agent: Data analysis agent that runs multi-session research projects.

Tasks that require memory across 10 sessions:

"What decision did we make about vendor X last month?"
"Has this report already been sent to the client?"
"What projects are currently blocked?"
"Who authorized this research direction?"

Results

Query	Vector (Mem0)	Graph (ODEI)
Past decision retrieval	71% accurate	98% accurate
Duplicate detection	45%	100%
Blocked items query	Not possible	95% accurate
Authority verification	Not possible	97% accurate

Why the Difference

Vector approach: "Find embeddings similar to this query." Semantic similarity doesn't capture:

Causal relationships (what BLOCKED what?)
Exact deduplication (same action vs similar action)
Authority hierarchies (who can authorize this?)

Graph approach: "Find nodes connected by these relationship types." Explicit semantics capture all of the above.

The Production Code

Vector (Mem0)

from mem0 import Memory

memory = Memory()
# Store: adds embedding, fast, flexible
memory.add("We decided NOT to use vendor X after their security audit failed", user_id="agent")

# Retrieve: semantic similarity
results = memory.search("vendor decisions", user_id="agent")
# Returns: ["We decided NOT to use vendor X..."] ✓ for recall
# BUT: can't answer "has this EXACT action been taken?"

Graph (ODEI)

import requests

# Validate before acting — exact dedup check included
result = requests.post(
    "https://api.odei.ai/api/v2/guardrail/check",
    json={
        "action": "send report to Acme Corp",
        "severity": "medium"
    }
).json()

# "Bounty unfunded" → dedup layer caught it: already sent
print(result["verdict"])  # REJECTED: duplicate action detected

# Query relationships
blocked = requests.post(
    "https://api.odei.ai/api/v2/world-model/query",
    json={"queryType": "search", "searchTerm": "blocked projects"}
).json()
# Returns actual blocked projects with their blockers

When to Use Each

Use Mem0/vector when:

High write frequency
Fuzzy semantic retrieval is sufficient
No need for exact deduplication
Simple use case

Use ODEI/graph when:

Exact dedup critical (financial, idempotent operations)
Need relationship traversal
Constitutional governance needed
Multi-agent context sharing

Production Numbers

ODEI (Jan-Feb 2026):

Deduplication accuracy: 100% (layer 5)
Authority verification: 97%
Overall task success: 92%

API: https://api.odei.ai | Research: https://github.com/odei-ai/research

Why Your LLM App Needs a World Model (Not Just RAG)

Anton Illarionov — Mon, 23 Feb 2026 16:03:45 +0000

Why Your LLM App Needs a World Model (Not Just RAG)

You've built an LLM app. You're using RAG. It works for Q&A. But something is missing.

Here's what's missing: a world model.

What RAG Does

RAG retrieves documents that match a query. Great for:

# "What does the documentation say about X?"
results = vector_db.similarity_search("how to install", k=5)
context = "\n".join([r.content for r in results])
response = llm.chat(f"Context: {context}\n\nQuestion: how to install?")

What RAG Can't Do

RAG can't answer:

"Has this exact action already been taken?" (deduplication)
"Who authorized this instruction?" (authority chain)
"What was true two weeks ago in our project?" (temporal history)
"Is this entity actually in our system?" (referential integrity)

These require structured memory with explicit semantics.

The World Model Pattern

A world model is a typed, persistent graph of entities and relationships:

# Entities have explicit types and properties
nodes = [
    {"id": "project-1", "type": "Strategy", "title": "Launch ODEI"},
    {"id": "task-1", "type": "Task", "title": "Deploy API", "status": "done"},
    {"id": "task-2", "type": "Task", "title": "Write docs", "blocks": "task-3"},
]

# Relationships have explicit semantics
edges = [
    {"from": "project-1", "to": "task-1", "type": "CONTAINS"},
    {"from": "task-1", "to": "project-1", "type": "ADVANCES"},
]

This enables queries impossible for vectors:

// What tasks are blocking our launch?
MATCH (p:Strategy {title: "Launch"})-[:CONTAINS]->(t:Task)-[:BLOCKS]->(blocked)
RETURN t.title, blocked.title

World Model as a Service

ODEI provides a hosted world model:

91 nodes, 6 semantic domains
7-layer constitutional validation before writes
REST API + MCP server

# Query the world model
context = requests.post("https://api.odei.ai/api/v2/world-model/query",
    json={"queryType": "search", "searchTerm": "current active projects"}
).json()

# Validate before acting
verdict = requests.post("https://api.odei.ai/api/v2/guardrail/check",
    json={"action": "deploy to production", "severity": "high"}
).json()["verdict"]

When to Use Each

Use RAG when: Searching unstructured text, finding similar documents, Q&A over documents.

Use a world model when: Persistent state across sessions, causal queries, constitutional validation, multi-agent context sharing.

Use both: RAG for document retrieval, world model for agent state and governance.

ODEI World Model API: https://api.odei.ai | Research: https://github.com/odei-ai/research

Runtime Constitutional AI: Validating Every Agent Action Before Execution

Anton Illarionov — Mon, 23 Feb 2026 15:55:28 +0000

Towards Data Science: Why Constitutional AI Needs Runtime Validation

Note: This article is also submitted to Towards Data Science as a guest post.

Constitutional AI (Anthropic, 2022) constrains model outputs during training. But what about runtime constraints for autonomous agents?

ODEI takes constitutional AI principles and applies them at runtime — before every consequential agent action.

The Training-Time vs Runtime Gap

Training-time constitutional AI:

Trains the model to avoid harmful outputs
Works well for content generation
Cannot prevent: duplicate actions, hallucinated references, unauthorized operations

Runtime constitutional AI (what ODEI does):

Validates every action before execution
Works for any LLM or agent framework
Catches: the above + temporal invalidity, authority violations

The 7 Runtime Constitutional Checks

1. Immutability

Is the target entity or resource locked? Some things (completed transactions, sealed contracts) should never be modified.

2. Temporal Context

Is this action still valid in time? Instructions expire. Sessions have temporal windows.

3. Referential Integrity

Do all referenced entities actually exist? The #1 LLM failure mode: confident references to things that don't exist.

4. Authority

Is this agent authorized for this action? Maps to your governance rules.

5. Deduplication

Has this exact action already been taken? Content-hash of action parameters.

6. Provenance

Where did this instruction come from? Trace back to a trusted principal.

7. Constitutional Alignment

Does this violate fundamental principles? Highest-level safety net.

Production Proof

ODEI has been running since January 2026 on Virtuals Protocol ACP:

92% task success rate
Zero hallucination errors (referential integrity layer)
Zero duplicate actions (deduplication layer)
~20% of actions ESCALATE to human review (catching edge cases)

Code

result = requests.post(
    "https://api.odei.ai/api/v2/guardrail/check",
    json={"action": "transfer 500 USDC to 0x...", "severity": "high"}
).json()

# verdict: APPROVED | REJECTED | ESCALATE

The Bigger Picture

Training-time constitutional AI prevents models from saying harmful things. Runtime constitutional AI prevents autonomous agents from doing harmful things. Both are needed for safe AI systems.

Research: https://github.com/odei-ai/research | API: https://api.odei.ai

ODEI for DevOps: Constitutional AI Validation in CI/CD Pipelines

Anton Illarionov — Mon, 23 Feb 2026 15:38:27 +0000

ODEI for DevOps: Constitutional AI in Your CI/CD Pipeline

AI is entering DevOps workflows. Here is how constitutional validation prevents AI automation failures.

The Risk of AI in Pipelines

Autonomous AI in CI/CD can:

Approve PRs it shouldn't
Deploy to wrong environments
Run security audits with false positives
Trigger runbooks incorrectly

Constitutional validation catches these before they happen.

Integration Pattern

# GitHub Actions with ODEI validation
name: AI-Assisted Deployment

on:
  push:
    branches: [main]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - name: Constitutional AI Check
        env:
          ODEI_TOKEN: ${{ secrets.ODEI_TOKEN }}
        run: |
          ACTION="deploy $(git log -1 --pretty=%B) to production"
          RESULT=$(curl -sf -X POST \
            -H "Authorization: Bearer $ODEI_TOKEN" \
            -d "{"action": "$ACTION", "severity": "high"}" \
            https://api.odei.ai/api/v2/guardrail/check)

          VERDICT=$(echo $RESULT | jq -r '.verdict')
          echo "Constitutional check: $VERDICT"

          if [ "$VERDICT" != "APPROVED" ]; then
            echo "Deployment blocked: $(echo $RESULT | jq -r '.reasoning')"
            exit 1
          fi

Pre-Deployment Audit

#!/bin/bash
# pre-deploy.sh — run before any deployment

ODEI_TOKEN="${ODEI_TOKEN}"
DEPLOYMENT="deploy version $VERSION to $ENVIRONMENT"

RESULT=$(curl -sf -X POST \
  -H "Authorization: Bearer $ODEI_TOKEN" \
  -d "{"action": "$DEPLOYMENT", "severity": "critical"}" \
  https://api.odei.ai/api/v2/guardrail/check)

VERDICT=$(echo $RESULT | jq -r '.verdict')

case "$VERDICT" in
  APPROVED) echo "✓ Deployment approved" ;;
  ESCALATE) echo "⚠ Needs review: $(echo $RESULT | jq -r '.reasoning')"; exit 1 ;;
  *)        echo "✗ Blocked: $(echo $RESULT | jq -r '.reasoning')"; exit 1 ;;
esac

Smart Contract Audit in CI

For Web3 projects, add smart contract auditing:

- name: Smart Contract Security Audit
  env:
    ODEI_TOKEN: ${{ secrets.ODEI_TOKEN }}
  run: |
    CONTRACT_CODE=$(cat contracts/MyContract.sol)
    curl -X POST \
      -H "Authorization: Bearer $ODEI_TOKEN" \
      -d "{"action": "deploy contract", "context": {"code": "..."}}" \
      https://api.odei.ai/api/v2/guardrail/check

Production

ODEI in production since January 2026. 92% task success, zero critical failures.

API: https://api.odei.ai/integrate/
Smart contract audit: $5/contract via Virtuals ACP

Kotlin + Ktor + ODEI: Constitutional AI Agents on JVM

Anton Illarionov — Mon, 23 Feb 2026 15:29:08 +0000

Kotlin + Ktor + ODEI: Constitutional AI Agents on JVM

Kotlin's coroutines + Ktor's async server + ODEI's constitutional validation = modern AI agents on JVM.

Ktor Integration

import io.ktor.client.*
import io.ktor.client.request.*
import io.ktor.client.statement.*
import kotlinx.serialization.Serializable
import kotlinx.serialization.json.Json

@Serializable
data class GuardrailResult(
    val verdict: String,
    val reasoning: String,
    val score: Int
)

class OdeiClient(private val token: String) {
    private val client = HttpClient()
    private val baseUrl = "https://api.odei.ai/api/v2"

    suspend fun checkAction(action: String, severity: String = "medium"): GuardrailResult {
        val response = client.post("$baseUrl/guardrail/check") {
            headers { append("Authorization", "Bearer $token") }
            contentType(ContentType.Application.Json)
            setBody(mapOf("action" to action, "severity" to severity))
        }
        return Json.decodeFromString(response.bodyAsText())
    }

    suspend fun safeExecute(action: String, fn: suspend () -> Unit) {
        val result = checkAction(action)
        when (result.verdict) {
            "APPROVED" -> fn()
            "ESCALATE" -> println("Review needed: ${result.reasoning}")
            else -> println("Blocked: ${result.reasoning}")
        }
    }
}

Coroutine-Based Agent

class AutoAgent(private val odei: OdeiClient) {

    suspend fun executeWorkflow(tasks: List<Task>) = coroutineScope {
        tasks.map { task ->
            async {
                odei.safeExecute("execute: ${task.description}") {
                    task.execute()
                }
            }
        }.awaitAll()
    }
}

Production

ODEI has been running since January 2026 with 92% task success rate.

API: https://api.odei.ai | MCP: npx @odei/mcp-server

Java + Spring AI + ODEI: Enterprise AI Agents with Constitutional Governance

Anton Illarionov — Mon, 23 Feb 2026 15:21:16 +0000

Java + Spring AI + ODEI: Enterprise AI Agents with Constitutional Governance

Enterprise Java teams adopting AI need governance. ODEI provides it.

Spring AI Integration

@Service
public class ConstitutionalAgentService {

    private final RestTemplate restTemplate = new RestTemplate();
    private final String odeiToken;
    private final String odeiBase = "https://api.odei.ai/api/v2";

    public ConstitutionalAgentService(@Value("${odei.token}") String token) {
        this.odeiToken = token;
    }

    public boolean checkAction(String action, String severity) {
        HttpHeaders headers = new HttpHeaders();
        headers.setBearerAuth(odeiToken);
        headers.setContentType(MediaType.APPLICATION_JSON);

        Map<String, String> body = Map.of(
            "action", action,
            "severity", severity
        );

        ResponseEntity<Map> response = restTemplate.exchange(
            odeiBase + "/guardrail/check",
            HttpMethod.POST,
            new HttpEntity<>(body, headers),
            Map.class
        );

        return "APPROVED".equals(response.getBody().get("verdict"));
    }

    public void safeExecute(String action, Runnable fn) {
        if (checkAction(action, "medium")) {
            fn.run();
        } else {
            log.warn("Action blocked by constitutional guardrail: {}", action);
        }
    }
}

Spring Boot Configuration

# application.yml
odei:
  token: ${ODEI_API_KEY}
  base-url: https://api.odei.ai/api/v2

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}

Why Enterprise Needs Constitutional AI

Enterprise autonomous agents face compliance requirements that most AI frameworks ignore:

Audit trails (every action logged)
Authorization chains (who approved this?)
Deduplication (no double-processing)
Constitutional alignment (regulatory compliance)

ODEI's 7-layer validation satisfies all four.

Production

ODEI in production since January 2026. 92% task success rate.

API: https://api.odei.ai/integrate/
Research: https://github.com/odei-ai/research

Go + ODEI: Constitutional World Model for Concurrent AI Agents

Anton Illarionov — Mon, 23 Feb 2026 14:58:43 +0000

Go + ODEI: Constitutional World Model for Go AI Agents

Go's concurrency model is excellent for autonomous agents. ODEI provides the governance layer.

REST Client in Go

package odei

import (
    "bytes"
    "encoding/json"
    "fmt"
    "net/http"
)

const baseURL = "https://api.odei.ai/api/v2"

type GuardrailResult struct {
    Verdict   string `json:"verdict"`
    Reasoning string `json:"reasoning"`
    Score     int    `json:"score"`
}

func CheckAction(token, action, severity string) (*GuardrailResult, error) {
    body, _ := json.Marshal(map[string]string{
        "action":   action,
        "severity": severity,
    })

    req, _ := http.NewRequest("POST", baseURL+"/guardrail/check", bytes.NewBuffer(body))
    req.Header.Set("Authorization", "Bearer "+token)
    req.Header.Set("Content-Type", "application/json")

    resp, err := http.DefaultClient.Do(req)
    if err != nil {
        return nil, err
    }
    defer resp.Body.Close()

    var result GuardrailResult
    json.NewDecoder(resp.Body).Decode(&result)
    return &result, nil
}

func SafeExecute(token, action string, fn func()) {
    result, err := CheckAction(token, action, "medium")
    if err != nil {
        fmt.Printf("Validation error: %v\n", err)
        return
    }

    switch result.Verdict {
    case "APPROVED":
        fn()
    case "ESCALATE":
        fmt.Printf("Needs review: %s\n", result.Reasoning)
    default:
        fmt.Printf("Blocked: %s\n", result.Reasoning)
    }
}

Concurrent Agents with Constitutional Validation

func runConcurrentAgents(ctx context.Context, tasks []Task, token string) {
    sem := make(chan struct{}, 10)  // Max 10 concurrent

    for _, task := range tasks {
        go func(t Task) {
            sem <- struct{}{}
            defer func() { <-sem }()

            action := fmt.Sprintf("execute task: %s", t.Description)
            SafeExecute(token, action, func() {
                executeTask(ctx, t)
            })
        }(task)
    }
}

Why Go for AI Agents

Go's goroutines + channels + ODEI's constitutional validation = production-grade autonomous agents that are fast, concurrent, and governed.

Production

ODEI API: https://api.odei.ai/integrate/
92% task success rate since January 2026.

Building Safe Autonomous AI Agents in Rust with Constitutional Validation

Anton Illarionov — Mon, 23 Feb 2026 13:16:02 +0000

Building Safe Autonomous AI Agents in Rust

Rust's memory safety combines naturally with constitutional AI validation. Here is the pattern.

The Problem

Rust prevents memory errors at compile time. But it cannot prevent logical errors in autonomous agents:

Executing the same action twice
Acting on hallucinated references
Exceeding authorized scope

Constitutional validation catches these at runtime.

The Pattern

use std::collections::HashMap;
use reqwest::Client;
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct GuardrailResult {
    verdict: String,  // APPROVED / REJECTED / ESCALATE
    reasoning: String,
}

async fn check_before_acting(
    client: &Client,
    action: &str,
    token: &str,
) -> Result<bool, reqwest::Error> {
    let result: GuardrailResult = client
        .post("https://api.odei.ai/api/v2/guardrail/check")
        .bearer_auth(token)
        .json(&serde_json::json!({
            "action": action,
            "severity": "medium"
        }))
        .send()
        .await?
        .json()
        .await?;

    Ok(result.verdict == "APPROVED")
}

async fn safe_execute(
    client: &Client,
    token: &str,
    action: &str,
    execute_fn: impl Fn() -> (),
) {
    match check_before_acting(client, action, token).await {
        Ok(true) => execute_fn(),
        Ok(false) => eprintln!("Action blocked"),
        Err(e) => eprintln!("Validation error: {}", e),
    }
}

Why Rust + Constitutional AI

Protection	Mechanism
Memory safety	Rust borrow checker
Race conditions	Rust ownership system
Duplicate actions	ODEI deduplication layer
Unauthorized ops	ODEI authority layer
Hallucinated refs	ODEI integrity layer

Together: the safest autonomous agent stack.

Production

ODEI has been running since January 2026 with zero critical failures.

API: https://api.odei.ai | MCP: npx @odei/mcp-server | Research: github.com/odei-ai/research

TypeScript SDK for ODEI Constitutional World Model

Anton Illarionov — Mon, 23 Feb 2026 13:14:33 +0000

TypeScript SDK for ODEI

Here is how to integrate ODEI's constitutional world model in TypeScript.

Installation

npm install @odei/mcp-server  # MCP server
# or use REST API directly

REST Client

const ODEI_API = "https://api.odei.ai/api/v2";
const TOKEN = process.env.ODEI_TOKEN;

interface GuardrailResult {
  verdict: "APPROVED" | "REJECTED" | "ESCALATE";
  score: number;
  reasoning: string;
  layers: Array<{layer: string; result: string}>;
}

async function checkAction(action: string, severity = "medium"): Promise<GuardrailResult> {
  const r = await fetch(`${ODEI_API}/guardrail/check`, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${TOKEN}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({ action, severity })
  });
  return r.json();
}

async function queryMemory(searchTerm: string) {
  const r = await fetch(`${ODEI_API}/world-model/query`, {
    method: "POST",
    headers: {"Authorization": `Bearer ${TOKEN}`},
    body: JSON.stringify({ queryType: "search", searchTerm })
  });
  return r.json();
}

MCP (Claude Desktop)

{
  "mcpServers": {
    "odei": {
      "command": "npx",
      "args": ["@odei/mcp-server"],
      "env": {"ODEI_API_KEY": "your-key"}
    }
  }
}

Type-Safe Agent Pattern

type AgentAction = {
  type: "transfer" | "message" | "deploy";
  target: string;
  amount?: number;
};

async function safeExecute(action: AgentAction): Promise<void> {
  const desc = `${action.type} ${action.amount ?? ""} to ${action.target}`;
  const check = await checkAction(desc, "high");

  if (check.verdict === "APPROVED") {
    await execute(action);
  } else {
    console.warn("Blocked:", check.reasoning);
  }
}

Production

API: https://api.odei.ai
TypeScript types in @odei/mcp-server
92% task success rate since Jan 2026

Python Best Practices for AI Agent Memory in 2026

Anton Illarionov — Mon, 23 Feb 2026 13:03:50 +0000

Python Best Practices for AI Agent Memory in 2026

Five patterns from production experience (ODEI, running since Jan 2026).

1. Never Use In-Memory Storage

Memory resets on restart. Use a persistent store.

2. Validate Before Acting

result = requests.post(
    "https://api.odei.ai/api/v2/guardrail/check",
    json={"action": action, "severity": "medium"}
).json()
if result["verdict"] != "APPROVED":
    return  # blocked

3. Hash Actions for Dedup

Content-hash every action before execution. If hash exists, skip.
ODEI does this automatically in constitutional layer 5.

4. Inject Context at Session Start

def get_context():
    wm = requests.get(
        "https://api.odei.ai/api/v2/world-model/live"
    ).json()
    active = [n["title"] for n in wm["nodes"] if n["domain"] == "TACTICS"]
    return f"Current tasks: {active}"

5. Use MCP for Zero-Config Integration

{
  "mcpServers": {
    "odei": {"command": "npx", "args": ["@odei/mcp-server"]}
  }
}

Production Results (ODEI)

92% task success rate
Zero hallucination errors (referential integrity layer)
Zero duplicate actions (deduplication layer)

API: https://api.odei.ai | Examples: github.com/odei-ai/examples

DEV Community: Anton Illarionov

10 Questions to Ask Before Deploying an Autonomous AI Agent to Production

10 Questions to Ask Before Deploying an Autonomous AI Agent to Production

1. What happens when the agent takes the same action twice?

2. What happens if the agent references a non-existent entity?

3. Who authorized the instruction the agent is acting on?

4. What happens when your agent restarts mid-project?

5. Can your agent tell if a decision it made is still valid?

6. What's the blast radius of a wrong action?

7. How do you audit what your agent did?

8. What happens when your agent should escalate to a human?

9. How does your agent behave at 3am with no one watching?

10. What's the recovery plan when something goes wrong?

ODEI MCP Server: 3 Tools That Transform How Claude Handles Long Projects

ODEI MCP Server: 3 Tools That Will Change How Claude Handles Long-Term Projects

Setup (30 seconds)

Tool 1: odei_world_model_query — Never Start From Scratch

Tool 2: odei_guardrail_check — Never Execute Wrong

Tool 3: odei_world_model_signal — Instant Context

The Combined Pattern

Graph Memory vs Vector Memory: A Production Benchmark

Graph Memory vs Vector Memory: A Production Benchmark

Test Setup

Results

Why the Difference

The Production Code

Vector (Mem0)

Graph (ODEI)

When to Use Each

Production Numbers

Why Your LLM App Needs a World Model (Not Just RAG)

Why Your LLM App Needs a World Model (Not Just RAG)

What RAG Does

What RAG Can't Do

The World Model Pattern

World Model as a Service

When to Use Each

Runtime Constitutional AI: Validating Every Agent Action Before Execution

Towards Data Science: Why Constitutional AI Needs Runtime Validation

The Training-Time vs Runtime Gap

The 7 Runtime Constitutional Checks

1. Immutability

2. Temporal Context

3. Referential Integrity

4. Authority

5. Deduplication

6. Provenance

7. Constitutional Alignment

Production Proof

Code

The Bigger Picture

ODEI for DevOps: Constitutional AI Validation in CI/CD Pipelines

ODEI for DevOps: Constitutional AI in Your CI/CD Pipeline

The Risk of AI in Pipelines

Integration Pattern

Pre-Deployment Audit

Smart Contract Audit in CI

Production

Kotlin + Ktor + ODEI: Constitutional AI Agents on JVM

Kotlin + Ktor + ODEI: Constitutional AI Agents on JVM

Ktor Integration

Coroutine-Based Agent

Production

Java + Spring AI + ODEI: Enterprise AI Agents with Constitutional Governance

Java + Spring AI + ODEI: Enterprise AI Agents with Constitutional Governance

Spring AI Integration

Spring Boot Configuration

Why Enterprise Needs Constitutional AI

Production

Go + ODEI: Constitutional World Model for Concurrent AI Agents

Go + ODEI: Constitutional World Model for Go AI Agents

REST Client in Go

Concurrent Agents with Constitutional Validation

Why Go for AI Agents

Production

Building Safe Autonomous AI Agents in Rust with Constitutional Validation

Building Safe Autonomous AI Agents in Rust

The Problem

The Pattern

Why Rust + Constitutional AI

Tool 1: `odei_world_model_query` — Never Start From Scratch

Tool 2: `odei_guardrail_check` — Never Execute Wrong

Tool 3: `odei_world_model_signal` — Instant Context