Building Structured Inter-Agent Communication: A Practical Guide

Albert zhang — Tue, 28 Apr 2026 11:04:12 +0000

Every multi-agent tutorial shows "Agent A talks to Agent B." None show how to keep that conversation reliable at scale.

The Problem with String-Based Agent Chat

# What most frameworks do:
result = agent_a.run("Analyze this and tell agent_b what to do")
agent_b.run(result)  # What if result is 2000 tokens? What if it omits context?

This breaks when:

Output exceeds token limits
Critical parameters get "summarized" away
Agent B parses instructions differently than intended

Our Solution: Typed JSON Contracts

Every agent in AgentForge declares its input schema:

{
  "agent": "risk_analyzer",
  "input": {
    "portfolio": ["AAPL", "TSLA"],
    "timeframe": "1d",
    "risk_threshold": 0.05
  },
  "expected_output": {
    "max_drawdown": "float",
    "sharpe_ratio": "float",
    "flags": ["string"]
  }
}

The orchestrator validates before execution. If agent A's output doesn't match agent B's input schema, the pipeline halts with a clear error — instead of agent B making a wrong inference.

Schema Enforcement at Runtime

from agentforge.core import Orchestrator, AgentContract

contract = AgentContract(
    input_schema={"query": str, "max_results": int},
    output_schema={"results": list, "confidence": float}
)

orch = Orchestrator()
orch.register("search_agent", search_fn, contract)

If search_fn returns "confidence": "high" instead of 0.92, the orchestrator flags it immediately.

Why This Matters

In production, you don't want agents to "kind of work." You want deterministic, debuggable, testable behavior. Typed contracts give you that.

Built with AgentForge. Open source. Production-tested.

https://github.com/agentforge-cyber/agentforge-mvp

Do you enforce schemas in your agent pipelines? Or do you trust the LLM to "figure it out"?

Posted on 2026-04-28 by the AgentForge team.

Real-Time Monitoring for AI Agents: Beyond Log Streaming

Albert zhang — Tue, 28 Apr 2026 08:47:13 +0000

Most agent monitoring is "log everything and grep later." That's not monitoring — that's archaeology.

What We Actually Need

Live execution view — Which agent is running right now?
State inspection — What data is Agent C holding?
Failure forensics — Why did Agent B timeout? What were its inputs?
Performance metrics — Per-agent latency, token usage, error rate

AgentForge's Monitoring Stack

Execution Trace (Structured JSON)

Every pipeline run generates a trace:

{
  "run_id": "uuid",
  "status": "completed",
  "agents": [
    {"name": "data_fetch", "status": "ok", "latency_ms": 1200, "tokens": 450},
    {"name": "analyzer", "status": "ok", "latency_ms": 3400, "tokens": 2100},
    {"name": "reporter", "status": "ok", "latency_ms": 890, "tokens": 1200}
  ]
}

WebSocket Dashboard

Real-time WebSocket feed showing:

Active agents (with heartbeat)
Queue depth per agent
Error rate (1-min sliding window)
Cost per run (token usage × model price)

Alert Rules

alerts:
  - condition: "agent.error_rate > 0.1"
    action: "circuit_breaker.open(agent)"
  - condition: "pipeline.latency > 30000"
    action: "pagerduty.notify(critical)"

Why This Matters for Production

When your agent pipeline runs 100+ times per day, "check the logs" doesn't scale. You need:

Proactive alerts (not reactive grep)
Structured traces (not raw text)
Per-agent metrics (not aggregate "it works")

We built AgentForge because nothing else gave us this.