# Why AI Agents Need a Common Observability Protocol — And What I Built About It

AI agents are everywhere. They're handling customer support, managing cloud infrastructure, processing financial transactions, orchestrating multi-step workflows. Every major platform is shipping agent capabilities — LangChain, CrewAI, AutoGen, OpenClaw, and dozens of custom enterprise stacks.

Multiple protocols power these agents. HTTP for API calls. MCP for tool use. A2A for agent-to-agent communication. The ecosystem is rich, growing fast, and increasingly autonomous.

But there's a fundamental gap that no one is addressing: **there is no common observability protocol for what AI agents actually do.**

## The Problem Is Bigger Than Logging

Every framework handles logging differently. Some write to stdout. Some use structured JSON. Some don't log at all. When an agent makes a decision — approving a refund, calling an external API, escalating a request to another agent — you're relying on whatever that specific framework decided to record, in whatever format it chose.

This isn't just an inconvenience. It's a reliability and trust problem that gets worse as agents scale.

Consider what's coming: personal agents negotiating with enterprise agents. Two different companies' agents transacting autonomously. Agents operating in healthcare, finance, defense, and critical infrastructure. In these environments, the question isn't "will something go wrong" — it's "when something goes wrong, how do we know what happened?"

AI hallucination remains an unsolved problem. An agent can hallucinate a tool call, fabricate an authorization, or misrepresent what the underlying LLM actually returned. Without a standardized, tamper-proof record, you'd never know.

And beyond hallucination, there's a security dimension. A rogue agent — whether compromised by a cyber attack or simply behaving unexpectedly — can take actions that are invisible if you don't have a reliable audit trail. In strategic and critical technology deployments, this isn't acceptable.

## What's Actually Needed

The problem isn't that individual frameworks need better logging. The problem is that we need a **common protocol** — something that works across frameworks, across protocols, and across organizational boundaries.

Think about the precedent. HTTP standardized web communication. OpenTelemetry standardized application observability. These weren't nice-to-haves — they were necessary infrastructure that enabled entire ecosystems to grow reliably.

Agent observability needs the same treatment. A standard format that any framework can write, any auditor can verify, and any tool can inspect. Framework-agnostic, protocol-agnostic, and tamper-evident by design.

## Agent History Protocol

This is why I built **Agent History Protocol (AHP)** — an open standard for tamper-evident, hash-chained recording of every AI agent action.

### How It Works

Every action an agent takes — HTTP calls, MCP tool use, A2A messages, LLM inference, authorization decisions — gets recorded as a cryptographically linked, append-only record. Each record contains a SHA-256 hash of the previous record, forming an unbreakable chain. If any record is modified, deleted, inserted, or reordered, the hash chain breaks and verification fails immediately.

The protocol defines three conformance levels:

**Level 1 — Recording.** Every action gets a hash-chained record with timestamps, sequence numbers, protocol type, tool name, parameter/result hashes, response times, and authorization details. This alone gives you a complete, verifiable audit trail.

**Level 2 — Signing.** Ed25519 cryptographic signatures on checkpoint records. This prevents an attacker from forging records that appear to come from a different agent. The Merkle root signature catches any tampering across the entire chain.

**Level 3 — Witness.** Independent witness servers that hold signed receipts of chain checkpoints. Even if an agent deletes its entire chain file, the witness has cryptographic proof that the chain existed and what it contained.

### What It Detects

AHP is designed to make tampering detectable, not to prevent bad actions. Like a flight recorder on an aircraft — it doesn't prevent the crash, but it makes it impossible to lie about what happened afterward.

Specifically, it detects modified records of any kind — tool calls, inferences, authorizations — because the hash chain breaks. It detects deleted records through sequence gaps and hash mismatches. It detects inserted fake records because the hash chain breaks at the insertion point. It detects reordered records through sequence and hash mismatches. And it detects an agent lying about what tool it called, what authorization it had, or what the LLM actually said.

With Level 2 signing, forged records from a different agent fail Ed25519 signature verification, and checkpoint tampering is caught by the Merkle root signature.

With Level 3 witnesses, even wholesale chain deletion is detectable — the witness server holds independent signed receipts proving the chain existed.

An enterprise auditor can run a single command — `ahp verify` — on any agent's chain and know immediately whether anything was altered.

### What It Doesn't Do

Transparency matters, so here's what AHP does not protect against. It doesn't encrypt the chain — an attacker with read access can see the records. It can't prevent a compromised agent from writing false records going forward, since the agent controls its own writer. And it can't prevent bad actions before they happen — the protocol records what happened, it doesn't gate what's allowed. Level 3 witnesses mitigate the risk of entire chain deletion, but the other limitations are inherent to the design. AHP is an accountability layer, not a prevention layer.

## Why Now

The convergence of three trends makes this urgent.

First, **agent proliferation**. The number of autonomous AI agents in production is growing exponentially. Every major cloud provider, every AI framework, and increasingly every enterprise is deploying agents for real work.

Second, **cross-boundary interaction**. Agents are starting to communicate across organizational boundaries — personal agents talking to enterprise systems, one company's agents negotiating with another's. Without a common observability format, there's no shared source of truth about what happened in these interactions.

Third, **regulatory pressure**. Governments and regulatory bodies are beginning to require auditability for AI systems. Having a standardized, tamper-evident recording format isn't just good engineering — it's going to be a compliance requirement.

## Getting Started

AHP is open source with SDKs for Python and TypeScript. Both support auto-instrumentation — drop-in interception of HTTP, MCP, and A2A calls with no code changes to your existing agent system.

```
pip install ahp
```

The CLI provides verification, inspection, gap detection, filtering, and export:

```
ahp verify --chain agent.ahp
ahp log --chain agent.ahp
ahp export --chain agent.ahp --format csv
```

The full specification, SDKs, and a multi-agent demo (with real LLM calls) are on GitHub:

**https://github.com/iamanandsingh/agent-history-protocol**

If you're building agents that make real decisions, I'd encourage you to think about observability as a first-class concern — not an afterthought. AHP is one approach to solving this. Star it, try it, break it. Contributions and feedback are welcome.

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*Anand Singh is the creator of Agent History Protocol and works on AI agent infrastructure and observability.*
