We’re at an inflection point for AI. The models have gotten good enough that the limiting factor is no longer intelligence—it’s action. AI can reason about what to do. The question is whether it can actually do it.
This is the tool use frontier. Here’s how we see it evolving.
From tools to toolchains
Today’s AI tool use is mostly single-step. AI calls one tool, gets a result, maybe calls another. The interactions are sequential and supervised.
Tomorrow’s tool use is compositional. AI orchestrates multiple tools in complex workflows, handling conditionals, loops, and error recovery. “Set up a new customer” becomes: check if they exist, create if not, configure billing, send welcome email, log the activity.
This requires more than tool access. It requires planning, execution monitoring, and error handling. AI needs to think in workflows, not just tool calls.
From supervised to autonomous
Current AI agents work with human oversight. They propose actions, humans approve, actions execute. This is appropriate for high-stakes operations but adds friction.
The future includes degrees of autonomy. Routine operations execute without approval. Novel or high-stakes operations pause for review. The boundary between automatic and supervised is configurable per operation, per user, per context.
Trust is earned progressively. An agent that consistently executes routine operations correctly earns autonomy for more complex tasks. Trust is dynamic, not binary.
From single-agent to multi-agent
Today’s AI is typically a single agent in a single context. You talk to Claude. Claude uses tools. The interaction is conversational.
Tomorrow’s AI includes specialized agents coordinating on complex tasks. An engineering agent files issues. A communications agent notifies stakeholders. An analytics agent monitors outcomes. Agents collaborate, delegate, and orchestrate.
This requires infrastructure for agent-to-agent communication, shared state, and coordination. MCP is a step in this direction, but multi-agent architectures need more.
What infrastructure enables
These futures require infrastructure that doesn’t fully exist yet.
Dynamic capability discovery: Agents need to find tools for tasks they haven’t encountered before. This is what intent-based discovery provides—semantic search over operations rather than static tool lists.
Secure credential delegation: As agents become more autonomous, credential security becomes more critical. Zero-knowledge approaches ensure AI can act without seeing secrets.
Audit and observability: Autonomous agents need logging, monitoring, and tracing. When something goes wrong, you need to understand what happened.
Policy and governance: Organizations need control over what agents can do. Rate limits, operation allowlists, approval workflows, compliance tracking.
Composition and orchestration: Multi-step, multi-agent workflows need primitives for sequencing, parallelism, error handling, and state management.
The opportunity
We’re building the infrastructure for AI agents to actually work—not demos, not toys, but production systems that take real action.
The opportunity is enormous. Every business process that involves API calls is a candidate for AI agent automation. Every integration between systems is a workflow an agent could orchestrate. Every manual task involving software is potentially delegatable.
The question is who builds the infrastructure to make this practical.
Where Toolcog fits
We started with the foundation: connecting AI to APIs securely and at scale. Three meta-tools, intent-based discovery, zero-knowledge credentials. This is the first layer.
The layers above—workflow orchestration, multi-agent coordination, progressive autonomy—build on this foundation. If AI can’t securely access APIs, nothing else works. If discovery doesn’t scale, agents are limited to predefined tools.
We’re building bottom-up. Get the foundation right first. The future layers follow.
What’s next
The models will keep improving. Tool use capabilities will become standard across AI platforms. The protocols—MCP and its successors—will mature and consolidate.
The differentiation shifts to infrastructure. Who provides the broadest access? The strongest security? The best discovery? The most reliable execution?
AI agents aren’t coming—they’re here. The question is what infrastructure enables them to be useful. That’s what we’re building.