AGNTCon + MCPCon Europe 2026

The next phase of agents is not one human supervising one model. It is multi-human, multi-agent work across teams, tools, and trust boundaries. The decisive moments are not model outputs; they are human approvals, edits, overrides, escalations, and handoffs, and the rationale behind them. Today those moments leak into chat logs, tickets, and application code, where they cannot be replayed or audited.

CHAP, the Collaborative Human-Agent Protocol, gives this shared workspace a protocol layer. A small Core (workspaces, participants, tasks, artefacts, and an append-only evidence log) carries composable profiles for review, structured override, operating modes, handoff, deliberation, identity, and signed audit. It composes with MCP and A2A rather than replacing them: MCP connects agents to tools, A2A connects agents to agents, CHAP lets humans and agents do accountable work together.

The talk covers the protocol gap, the Core primitives, a worked 'override as evidence' flow, and lessons from building the open reference implementations and conformance harness. Spec, code, and examples are public.

Paper: https://arxiv.org/abs/2606.09751
Repo: https://github.com/BrightbeamAI/chap

When AI writes 80–90% of the code on a team, peer review breaks. The pull request is produced by a machine you can't argue with, and the reviewer is auditing a diff instead of talking to a peer. Review has stopped feeling like collaboration, and most of us have started hating it.

We rebuilt how we deploy AI-assisted development in production. Human review moved off the diff and onto the plan: the intent written before any code gets generated. Engineers review the thinking they care about and let the agent fill in the gaps. When the PR lands, CI runs the usual checks plus an automated comparison against the approved plan. Only deviations route back to the original reviewer.

This is a case study in integrating AI into a real SDLC without breaking accountability, quality, or culture. I'll walk through what broke, what we automated, and what stayed human: an in-house MCP server for plan review in the IDE, deviation-checking on every PR, and cultural bets (everyone operates as a team lead; no questions until working code; customer context radiated to the whole team).

We massively improved our velocity and our lead time, and our engineers love the job again.

Integrating agentic AI into the UI easily leads to ad hoc integrations, tight coupling to backend technologies, and vendor lock-in. So how do we design scalable, maintainable interactions between agents and users?

This session shows how open standards like AG-UI, A2UI, and MCP Apps enable a protocol-driven approach to Agentic UI, establishing clear, message-based boundaries that decouple UI, agent logic, and tools. You’ll learn how to apply these standards, leveraging dynamic UI generation, tool integration, and Human-in-the-Loop patterns.

By the end, you’ll understand how to design Agentic UI using open standards and integrate them through clear, protocol-based boundaries.

Most teams shipping agents into production hit the same wall. The build is fast. The adoption is not. A solution that works in three weeks can take three months before operators trust it enough to actually use.

This talk is about closing that gap. Drawing on production deployments across fifteen-plus enterprise clients in logistics, wholesale, manufacturing, insurance, and finance, I'll walk through a four-stage model for moving agents from advisory to full autonomy without breaking trust along the way.

The talk goes deep on a single use case deployed across multiple clients: sales order processing. I'll cover what changed when we moved too quickly between stages and lost trust we had already earned, how we rebuilt it, and the operator-side patterns we now look for before progressing a stage.

The framework is protocol-agnostic. Whether teams are integrating agents through MCP, APIs, or a mix, the trust progression is the same. The talk should be useful for anyone building or deploying agent systems where humans remain in the loop and where adoption, not capability, is the binding constraint on impact.