Forge is preparing the requested surface and verifying the live route.
Forge is preparing the requested surface and verifying the live route.
Agents
No manual configuration. No marketplace browsing. No fixed capabilities. The agent acquires the right skill at the right moment — automatically.
Traditional agents require you to define capabilities upfront. If your agent handles email today and writes code tomorrow, you either build two agents or accept one will be poorly equipped. Forge eliminates this entirely.
CSM observes what an agent is trying to accomplish in real time and injects the relevant skill at the exact moment it's needed. The agent starts lean. As the task evolves, the platform equips it dynamically — not because you configured it, but because CSM learned what this type of task requires.
CTM predicts which tools will be needed before the agent finishes parsing your request. Connections are pre-warmed. Credentials are staged. The agent arrives at every task already prepared.
The Continuous Learning Engine has already learned your patterns — your domain vocabulary, your preferred workflows, your frequently used tools. Every subsequent interaction makes the agent more attuned to how you work.
From your perspective: one assistant that handles everything.
From the platform's perspective: a continuously self-equipping agent that acquires capability on demand without ever stopping to configure itself.
Traditional multi-agent platforms require you to design your team upfront. Define roles. Fix capabilities. Manage handoffs. Forge inverts this completely.
Every agent on a Forge team dynamically acquires capabilities through CSM. You don't need a research agent and a writing agent because every agent can do both — and CSM ensures it's equipped for whichever it's currently doing.
Context flows through Forge Memory Fabric and the ICE knowledge graph so any agent can pick up any task with full context — not a summary, not a handoff briefing, full context.
CLE learns which agents handle which task types more successfully. CSM begins pre-injecting relevant skills proactively based on performance data. Specialization emerges from real outcomes, not from upfront design.
The ForgeWeave graph maintains a live map of the team's collective capability state. Every agent knows what every other agent is working on. When a needed skill is in use by one agent, another picks up the task.
Concrete examples of self-organizing agent teams handling real work.
You describe the outcome: 'Handle support tickets, escalate billing issues, follow up on resolved tickets after 48 hours.' The team self-organizes. One agent triages incoming tickets. Another researches solutions in your knowledge base. A third drafts responses. All three have full conversation context. When the triage agent encounters a billing dispute, it acquires billing-specific skills from CSM without stopping.
You say: 'Review the open PRs, write tests for uncovered edge cases, update the API docs.' Three agents execute in parallel. The PR reviewer acquires security scanning skills when it finds a dependency vulnerability. The test writer acquires your testing framework conventions from CLE. The doc writer pulls your documentation style from prior entries. Results synthesize into a single coherent update.
You ask: 'Analyze our competitor pricing changes and draft a response strategy.' One agent monitors pricing pages via Forge Connect. Another cross-references with your historical pricing data. A third drafts the strategic response, informed by both data streams in real time. The team parallelizes automatically — no configuration, no workflow design.
Not a collection of individually configured processes executing in isolation. Your job is to describe the outcome you want. The team's job is to parallelize it, equip itself for it, and deliver it.
Build your first agent in minutes. Watch it acquire capabilities on its own. Add more agents and watch the team self-organize.