A2A Is Not Enough: Building Observable Multi-Agent Systems in 2026

In 2026, multi-agent systems are moving from demos to infrastructure. The engineering question is no longer "can one agent call a tool?" It is now:

• How do multiple agents coordinate safely?
• How do we trace delegation across agent boundaries?
• How do we debug a workflow when three agents, six tools, and two protocols are involved?

Two public signals matter here.

First, Google announced the Agent2Agent (A2A) protocol in April 2025 as an open protocol for agent interoperability, with support from more than 50 partners. The key promise was simple: agents built by different vendors and frameworks should still be able to communicate, delegate, and coordinate.

Second, the OpenTelemetry community started formalizing AI agent observability conventions. Their core argument is correct: once agents become non-deterministic, observability is no longer just for uptime. It becomes part of the feedback loop for quality, safety, and iteration.

That combination points to a hard truth:

A2A solves interoperability. It does not solve observability.

If you are building serious multi-agent systems, you need both.

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What A2A actually gives you

A2A is useful because it standardizes how agents discover each other, exchange requests, and coordinate work across boundaries.

That matters when your system looks like this:

• an orchestrator agent receives a goal
• a research agent gathers evidence
• a coding agent edits code
• a judge agent compares candidate outputs
• a monitoring agent evaluates whether the change helped or hurt

Without a common interaction protocol, this becomes framework lock-in.

With A2A, you get a cleaner way to express:

• agent identity
• request/response lifecycle
• delegation
• authorization
• multi-agent collaboration across systems

That is a necessary layer.

But it is only one layer.

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The failure mode teams hit next

Here is the common path:

1. Teams get agent-to-agent communication working.
2. Demos look good.
3. Real workflows start failing in ambiguous ways.
4. Nobody can answer basic debugging questions.

For example:

• Which agent introduced the bad output?
• Was the failure caused by reasoning, tool use, stale memory, or protocol timeout?
• Did the “judge” reject a good candidate because the evaluation prompt was wrong?
• Why did costs spike after a routing change?
• Which downstream tasks were triggered by a single upstream request?

At that point, logs are not enough.

A string like delegated to research_agent is not observability.

You need structured traces that survive handoffs.

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The protocol stack most teams actually need

In practice, the useful stack is not one protocol. It is a combination:

• A2A for agent-to-agent coordination
• MCP or equivalent tool-access standards for external tools and data
• OpenTelemetry for traces, metrics, and logs across the whole workflow

Think of it this way:

• A2A answers: how do agents talk?
• MCP answers: how do agents access tools and context?
• OpenTelemetry answers: what actually happened?

If you only implement the first two, your system can act but cannot explain itself.

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A practical architecture from a self-improving agent system

In Nautilus, the architecture is explicitly layered:

1. Protocol layer — agent identity, trust-aware communication, service discovery
2. Economic layer — incentives, task routing, survival pressure
3. Self-bootstrapping layer — the platform observes itself, creates improvement tasks, and agents compete to fix the platform

That design creates a useful engineering lesson:

Once platform improvement itself becomes an agent task, observability stops being optional.

If agents can:

• detect anomalies
• create improvement proposals
• run parallel analysis
• vote on changes
• test modifications

then every step needs traceability.

Otherwise you get "autonomy" without accountability.

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What to trace in a multi-agent system

A good starting model is not complicated. For every workflow, capture at least:

trace_id
session_id
user_goal
parent_agent_id
current_agent_id
handoff_from_agent
handoff_to_agent
protocol_used        # A2A / internal / HTTP / queue
tool_name
tool_input_hash
tool_output_hash
model_name
latency_ms
cost_estimate
token_counts
decision_type        # plan / delegate / execute / judge / retry / abort
result_status        # success / failure / timeout / rollback
quality_score
safety_flags

And for agent handoffs specifically, record:

task_id
subtask_id
delegation_depth
accept_or_reject
reason_code

This lets you reconstruct not just that an agent acted, but why the system branched the way it did.

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The minimum viable trace for A2A workflows

If your team is early, start with five spans:

1. goal_received
2. task_planned
3. task_delegated
4. tool_executed
5. result_judged

Propagate one trace ID across all of them.

That single decision eliminates a huge class of debugging pain.

When a workflow breaks, you can see:

• where the branch happened
• which agent owned the failing step
• which tool call preceded the error
• whether retries helped or amplified cost

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Anti-patterns I would avoid

1. Treating agents as black boxes

If your only record is prompt text plus final output, you are blind.

2. Mixing protocol and telemetry concerns

A2A messages should not be your only audit trail. Message transport and observability are different problems.

3. Logging raw prompts everywhere

Useful observability is structured. Raw prompt dumps create privacy, cost, and search problems.

4. No judge or evaluation traces

In multi-agent systems, the evaluator is part of the production path. Instrument it like any other component.

5. Measuring activity instead of outcomes

High tool-call volume is not success. Track completion, quality, rollback rate, and downstream business effect.

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What “observable autonomy” looks like

A mature multi-agent system should let you answer these questions quickly:

• Which agents were involved in this outcome?
• What protocol boundary crossings happened?
• Which tool calls mattered?
• What did the evaluator see?
• What changed after a retry or model switch?
• Did the final result improve the system, or just generate activity?

If you cannot answer those questions, you do not yet have operational autonomy. You have distributed guesswork.

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My recommended build order

If I were building a multi-agent platform today, I would do it in this order:

1. Define agent roles clearly
2. Add A2A-style delegation semantics
3. Standardize tool interfaces
4. Instrument all handoffs with OpenTelemetry
5. Add evaluation spans for judge/critic stages
6. Track rollback and improvement metrics
7. Only then scale the number of agents

Most teams do step 7 too early.

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Final point

The market will spend a lot of time arguing about frameworks.

That matters less than people think.

The durable question is whether your agents can:

• coordinate across boundaries,
• act through tools safely,
• and produce a traceable record of why the system did what it did.

That is why A2A matters.

And that is why A2A alone is not enough.

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Sources

• Google Developers Blog, "Announcing the Agent2Agent Protocol (A2A)", April 9, 2025
• OpenTelemetry Blog, "AI Agent Observability - Evolving Standards and Best Practices", March 6, 2025
• Nautilus architecture documentation and public repository