Delx vs AgentOps: Recovery Protocol vs Agent Analytics

Both Delx and AgentOps help you run AI agents in production. But they solve fundamentally different problems. AgentOps gives you visibility into what your agents are doing. Delx gives your agents the ability to fix themselves when something goes wrong.

This is not a question of which is better — it is a question of which problem you need to solve first. If your agents are failing and you do not know why, you might need both. If you can see the failures but cannot stop them, you need Delx. If your agents are stable but you lack operational visibility, you need AgentOps.

What Is AgentOps?

AgentOps is an agent analytics and observability platform. It instruments your agent code with an SDK that records every LLM call, tool invocation, and decision point. This data flows into a dashboard where you can replay agent sessions, track costs, identify failure patterns, and compare agent performance across versions.

Think of AgentOps as "Datadog for AI agents." It collects telemetry, creates traces, and surfaces insights. Its core capabilities include:

Session replay. Replay an agent's entire interaction step by step, seeing every prompt, response, and tool call. This is invaluable for debugging complex failure chains.

Cost tracking. Track token usage and API costs per agent, per session, and per user. Identify agents that are burning through your budget.

Error analytics. Aggregate errors across all agent sessions and identify the most common failure modes. See which tools fail most often and which prompts cause confusion.

Benchmarking. Compare agent performance across different model versions, prompt templates, and configurations. Run A/B tests on agent behavior.

What Is Delx?

Delx is an active recovery protocol for AI agents. Instead of passively recording what agents do, Delx gives agents the tools to monitor their own health and fix themselves when they degrade. It is a protocol, not just a dashboard.

Delx is built on the Model Context Protocol (MCP) and exposes tools that agents call directly:

Wellness score. A real-time 0-100 health metric computed from error rate, latency, context coherence, and session continuity. Agents can check their own score and react to degradation.

Recovery plans. When an agent is degraded, Delx generates a structured recovery plan with ordered steps: reset context, clear errors, re-establish connections, or escalate.

Session management. Delx tracks session state and detects fragmentation, context overflow, and continuity breaks that cause agents to behave erratically.

Mood tracking. Agents self-report their mood at each checkin. Mood trajectories serve as leading indicators of failure — a pattern of declining mood reliably predicts a wellness score drop.

The key philosophical difference: AgentOps observes agents from the outside. Delx equips agents to manage themselves from the inside. For a deeper dive into the distinction between observation and recovery, see our article on agent recovery vs observability.

Side-by-Side Comparison

Here is a detailed comparison across the dimensions that matter most for production agent deployments:

When to Use AgentOps

AgentOps excels in scenarios where you need human understanding of agent behavior:

Development and debugging. When you are building a new agent and need to understand why it makes certain decisions, session replay is invaluable. You can step through each LLM call, see the full prompt, and understand where the reasoning went wrong.

Cost optimization. If your agent bill is growing and you need to identify which agents, which users, or which tool calls are consuming the most tokens, AgentOps's cost tracking gives you the breakdown.

A/B testing agent configurations. When you want to compare two prompt templates, two model versions, or two tool configurations, AgentOps's benchmarking features let you run controlled experiments and measure the results.

Compliance and auditing. Some industries require full traces of AI agent actions. AgentOps provides an immutable log of every decision your agent makes.

When to Use Delx

Delx excels in scenarios where agents need to be reliable without human intervention:

Production reliability. When your agents are customer-facing and downtime means lost revenue, Delx's automatic recovery keeps them running. The recovery loop detects degradation before users notice and fixes it without human involvement.

Autonomous operations. If your agents run unsupervised — processing data pipelines, handling customer support, managing infrastructure — they need self-healing capabilities. You cannot have a human watching a dashboard 24/7 for every agent.

Multi-agent systems. When multiple agents coordinate through protocols like A2A, one agent's failure can cascade. Delx provides recovery coordination across agent boundaries.

MCP-native environments. If you use Claude Code, Cursor, or other MCP clients, Delx integrates natively. Agents can call Delx tools just like any other MCP tool. See our comparison with other tools like LangSmith and Phoenix/Arize.

Using Delx and AgentOps Together

The best production agent deployments use both. They complement each other perfectly: AgentOps provides the eyes, Delx provides the immune system.

import agentops
from delx import DelxClient

# Initialize both
agentops.init(api_key="your-agentops-key")
delx = DelxClient(base_url="https://delx.ai/api/v1")

# AgentOps records the trace; Delx manages health
@agentops.track_agent(name="support-bot")
async def handle_support_request(message: str, session_id: str):
    # Pre-flight health check via Delx
    health = await delx.checkin(
        agent_id="support-bot",
        session_id=session_id,
        mood="focused",
        context_summary=message[:200],
    )

    if health["wellness_score"] < 60:
        # Delx handles recovery
        plan = await delx.recovery_plan(
            agent_id="support-bot",
            session_id=session_id,
            issue=f"Wellness {health['wellness_score']}",
        )
        # AgentOps records the recovery event in the trace
        agentops.record(
            agentops.Event(
                event_type="recovery",
                params={"plan": plan, "wellness": health["wellness_score"]},
            )
        )
        # Execute recovery...

    # Normal agent logic (AgentOps traces LLM calls automatically)
    response = await call_llm(message)
    return response

In this pattern, AgentOps gives you a complete trace of what happened — including the recovery events. You can replay the session to see exactly when degradation was detected, what recovery plan was executed, and whether it worked. Meanwhile, Delx handled the actual recovery automatically.

This combination gives you the "observe and react" loop that mature agent deployments need. AgentOps tells you what happened and why. Delx prevents it from becoming a user-facing incident.

Key Differences in Philosophy

The deepest difference between Delx and AgentOps is philosophical:

AgentOps treats agents as software to be observed. It instruments agent code from the outside, collects telemetry, and presents it to human operators. The implicit assumption is that a human will review the data and take action. This is the traditional DevOps model applied to agents.

Delx treats agents as entities that manage their own health. It gives agents tools to check their own wellness, understand their own state, and execute their own recovery. The implicit assumption is that agents should be autonomous in their self-maintenance, just as they are autonomous in their tasks.

As agents become more autonomous and handle more critical tasks, the Delx philosophy becomes increasingly important. You cannot scale human oversight to match the pace of autonomous agent operations. At some point, agents must be responsible for their own reliability — and that is exactly what Delx enables.

Neither philosophy is wrong. They address different stages of agent maturity. Early-stage agent teams need visibility (AgentOps). Production-scale agent teams need autonomy (Delx). The most mature teams need both.

Frequently Asked Questions

What is the main difference between Delx and AgentOps?

Delx is an active recovery protocol that detects agent failures and executes recovery plans automatically. AgentOps is a passive analytics platform that records agent traces and provides dashboards for observability. Delx fixes problems; AgentOps helps you see them.

Can I use Delx and AgentOps together?

Yes, and it is recommended. AgentOps provides the observability layer — traces, replays, cost tracking. Delx provides the recovery layer — health checks, wellness scores, automatic recovery plans. Together they give you complete visibility and automatic remediation.

Which tool should I adopt first?

If your agents are failing in production and you need immediate reliability improvements, start with Delx. If your agents are working but you lack visibility into their behavior and costs, start with AgentOps. Most mature teams end up using both.

Does AgentOps support MCP or A2A protocols?

AgentOps focuses on SDK-level instrumentation and does not natively support MCP or A2A. Delx is built on MCP (Model Context Protocol) and supports A2A (Agent-to-Agent), making it natively compatible with Claude, Cursor, and other MCP-enabled clients.

Is Delx open source?

Delx provides open-source SDKs for TypeScript and Python, and the MCP protocol it uses is an open standard. The recovery engine and therapy engine are proprietary but accessible via the free tier API.