Inside the Agent Architecture: Multi-Agent Orchestration in Action

(Webinar Recap)

TL;DR

• Agents ≠ chatbots. True agents decide, reason across multiple steps, and take actions across systems.

• Value compounds with orchestration. The Agent Maturity Curve moves from single-task agents → multiple agents → agents that coordinate and share context.

• Readiness matters: data quality, connected systems, and continuous learning determine outcomes.

• Trust is designed, not assumed: disclosure, rate-limited outreach, language adaptability, audit trails, and human-in-the-loop are essential guardrails.

• Live scenario: four agents collaborated to fix equipment ID, confirm new ETA, reschedule, and notify stakeholders - an end-to-end exception resolution.

“Agents are teammates, not toys. When they’re connected to the right data and guardrails, they lift the repetitive work so people can steer high-value outcomes.” — Kris Glotzbach, CRO, LunaPath

What this webinar covered

Hosts & speakers:

• Eric Fullerton, VP Product Marketing, project44

• Nick Ruggiero, Director of Product, project44

• Kris Glotzbach, CRO, LunaPath

This was Part 2 of project44’s three-part series, focused on agent workflows, orchestration, and a live demo of exception handling.

What makes something an “agent” (and not a chatbot)

A true agent has three traits:

1. Decisioning: evaluates data and chooses the next step.

2. Multi-turn reasoning: runs a multi-step process, not a single response.

3. Action: takes steps autonomously (calls, emails, updates systems) rather than returning text alone.

This matters because freight ops require closed-loop execution - not just suggestions.

Related reading:

• AI Agents vs. Chatbots in Logistics: What’s the Difference?

• Specialized AI Agent vs. Super-Agent: Why Focus Wins in Freight Automation

The Agent Maturity Curve (from pilots to orchestration)

1. Stage 1—Single task, single agent: narrow wins (e.g., tracking updates or email summaries).

2. Stage 2—Single agent, multiple tasks: broader scope but still isolated.

3. Stage 3—Multiple agents (multi-vendor), discrete tasks: progress, but fragmentation risk.

4. Stage 4—Multiple agents across domains: more flexible, still limited context sharing.

5. Stage 5—Breakthrough orchestration: agents share context, trigger each other, and coordinate toward a shared goal (detect → decide → act → update).

Takeaway: Orchestration is where speed, accuracy, and scale converge.

Enablers: data, connections, and learning

• Data readiness: Agents only act as well as the timeliness and accuracy of the data they see.

• Connected systems: If agents can’t write back to TMS/CRM/ERP, they can’t close the loop.

• Continuous learning: Feedback loops and performance telemetry improve outcomes over time.

Related reading:

• The CORE Framework (Context, Outcome, Reasoning, Evolution)

• AI Guardrails in Freight: Control & Compliance

Demo highlight: four agents, one exception, zero chaos

Scenario: A cross-border shipment created multiple exceptions (missing equipment ID, late ETA, appointment miss risk, and customer comms).

How the agents worked in sequence and in parallel:

1. Equipment ID agent retrieved the correct identifier to restore visibility.

2. ETA agent contacted the carrier, captured the root cause (customs documentation), and obtained a new ETA.

3. Appointment agent recognized the miss risk and rescheduled automatically.

4. Communication agent updated shipper CS and carrier with the new appointment + delivery number.

Bonus: The system created a human task to review cross-border documentation to reduce future dwell, turning resolution into continuous improvement.

Trust & guardrails: how to move fast without breaking relationships

Top questions addressed during Q&A:

• Will agents spam carriers?

  No. Outreach is rate-limited, batched when appropriate, and channel-aware (e.g., consolidate 10 issues into one email). Cadence varies by urgency, time-of-day, region, and carrier preference.

• Voice vs. email vs. SMS—what’s best?

  It depends on urgency, region/language, and use case. Critical exceptions → voice; after-hours → email; quick confirmations → SMS.

• Language handling and disclosure?

• Agents adapt to language in real time and disclose they’re automated. Transparency increases completion rates and trust.

• Auditability?

• Transcripts, timestamps, task IDs, and write-backs to the system of record support internal reviews and customer assurance.

Related reading (internal links):

• AI Forklifts for Data: Why Humans Still Steer

• How to Automate POD Retrieval in Your TMS

Why orchestration (not a single “super-agent”) wins

• Precision: Specialized agents tuned to a single outcome reduce errors.

• Latency: Narrow scope = faster responses for time-sensitive SLAs.

• Cost: Specialists minimize compute and lower cost per load.

• Measurability: Clear KPIs (SLA hits, hours saved, dispute reduction) make ROI obvious.

What to do next (90-day roadmap)

1. Pick one workflow (e.g., POD retrieval or check-call status cadence).

2. Integrate write-back so your TMS remains the source of truth.

3. Define guardrails (disclosure, cadence limits, escalation paths).

4. Measure weekly: % SLA hits, hours saved, exception dwell time, dispute rate.

5. Scale to the next agent (appointments, notifications, invoice audit).

Ready to see multi-agent orchestration in action? Book a demo to explore one workflow (status or PODs) and measure results in week one.