Running Multi-Agent Workflows

Running Multi-Agent Workflows

One of the most powerful features in Forge is the ability to run multi-agent workflows — scenarios where multiple agents collaborate to answer a complex question or complete a layered analysis.

Instead of getting a single perspective, Forge can activate several agents in parallel, let them handle their parts, and then return a unified result. This makes it feel less like a chatbot and more like a research team working together.

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What Is a Multi-Agent Workflow?

A multi-agent workflow is triggered when:

• A user question touches multiple areas (token + wallet + LP)

• An agent explicitly calls another agent for follow-up context

• The system detects a chain of dependencies in the logic

Each agent operates independently, using its own logic and prompt, but Forge’s query handler merges the outputs into one final message.

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When They Activate

Multi-agent workflows activate automatically in questions like:

• “Is this token safe and who deployed it?”

• “What’s this wallet doing and did they rug before?”

• “Why is this token pumping and who bought in?”

Here’s how Forge breaks it down:

1. TokenAgent scans LP, contract state, volume

2. WalletAgent profiles the deployer and recent buyers

3. SniperAgent checks for known sniper activity

4. LPAgent analyzes LP structure, burn, and lock

Forge takes all the replies, filters overlap, and composes a final, clear answer.

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How Agents Share Context

Each agent receives a slice of the context relevant to its role. For example:

• TokenAgent gets token metadata, deploy time, volume, supply

• WalletAgent gets the deployer address and wallet history

• LPAgent gets liquidity movement and contract permissions

They don’t step on each other’s data. This separation keeps each analysis focused.

After all responses are in, the Query Handler:

• Prioritizes the most urgent signals (e.g. LP unlocked, rug deployer)

• Removes redundancy (don’t repeat “LP not burned” 3 times)

• Formats the combined result clearly

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Workflow Example

You ask:

“What’s the deal with $ZAP?”

Forge triggers:

• TokenAgent: “$ZAP launched 4 min ago, 12 SOL LP, no burn, ownership not renounced.”

• WalletAgent: “Deployer is 111abc, previously launched 2 tokens. One rugged, one low volume.”

• SniperAgent: “Wallet 555abc entered with 20 SOL. Known sniper, sold last 5 tokens within 5 mins.”

Final reply:

“$ZAP launched 4 mins ago with 12 SOL LP (unburned, ownership not renounced). Deployer has a mixed history including 1 rug. Known sniper 555abc just entered with 20 SOL. High risk.”

You get one answer, but three agents did the work.

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Chaining Custom Agents

You can create your own workflows using custom agents. For example:

1. An “InfluencerAgent” watches Telegram or Twitter

2. When a deployer is flagged, it triggers a “ContractScanAgent”

3. If red flags appear, it sends an alert through a “NotifierAgent”

Each stage in the chain activates the next, building a full lifecycle of detection, verification, and alert.

This is useful for:

• Private trading groups

• DAO security desks

• Automated due diligence pipelines

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Benefits

• Speed – Parallel agents reduce wait time

• Depth – Each agent is specialized and focused

• Clarity – Results are organized, not dumped

• Scalability – Add more agents as your needs grow

Forge becomes more intelligent not by having one massive model, but by letting many smaller processes cooperate efficiently.