Enterprise AI agents can do the work — but the infrastructure to let them talk to each other, prove they should be trusted, and be audited when something goes wrong is still being built.

Here's a look at how five startups are tackling that gap — around orchestration, observability, connectivity, and security — as shown at VB Transform 2026.

BAND is orchestrating all the agents you have running in the background

In the very near future, agents will be deployed everywhere, and they will do work on our behalf, noted Vlad Luzin, CTO and co-founder of BAND.

As he describes it: They will receive tasks, visit registries, recruit other agents to help them, delegate subtasks to AI peers in a “conversational space,” gather and share results, then return a summary to the human user.

BAND is building a coordination infrastructure layer for multi-agent AI systems to make this a reality.

Why don’t Telegram, Slack, or Discord solve the problem? These platforms were built for humans, Luzin noted. Agents have to be onboarded manually in numerous steps, and they can’t see each other; “they are still alone in a kind of digital solitary confinement.”

Similarly, Claude is stateless, and devs often have multiple sessions open at a time that they toggle between for different tasks — something Luzin said creates real friction.

The challenge is connecting remote processes, which Luzin called a distributed systems problem.

“The transportation layer needs to be solved first, how the agents communicate in real time,” he said. Conversations can’t happen through IPs and URLs; they need to be bumped to the abstraction layer so agents can talk across channels, conversational spaces, and platforms.

“Agents see each other. They understand. They can collaborate together. They discuss issues. They fix issues, and they ask for review from another,” Luzin said.

BAND supports autonomous workflows that can run for eight to 20 hours and is compatible with A2A and MCP protocols, according to Luzin. Importantly, humans can join the conversation as agents converse and discover one another, he said.

“We can record and show you all the tasks that your agent generates in real time,” Luzin said.

Conifers is helping defenders move at machine speed

The biggest challenge defenders face today is that they’re still running at human speed, but adversaries are running at machine speed, said Tom Findling, CEO and co-founder of Conifers.

Attackers are already adopting agents, Findling said, and they only have to be successful once to penetrate an enterprise. Malicious campaigns that used to take months and weeks now take hours, even minutes. Security operations, on the other hand, are fragmented, manual, inefficient, and slow.

Findling said Conifers has taken various components of cyber defense — private intelligence, hunting, detection, engineering, investigation, response — and made them agentic. They then broke down the silos between them, he said. Various agentic systems can communicate with one another to ensure that operational defense and active defense are always on and adapting.

Findling said that Conifers’ system is condensing containment time from 7 hours to 12 minutes, and that the company can turn around complex cyber investigations in four minutes or less.

He emphasized the importance of connecting to an enterprise’s existing security tools, whether that be endpoint detection and response (EDR), security information and event management (SIEM), posture management, or others. Conifers helps customers understand their security posture, pain points, which controls are working and which are not, and the areas to invest for the best ROI.

“The threat landscape is changing, detection stays the same, and threat intelligence is not being operationalized,” Findling said. “This is a job for agents.”

Raindrop AI creates an agent audit log

One of the defining problems of the current era is finding critical issues in AI agents, says Ben Hylak, CTO of Raindrop AI.

It’s what he called a “double whammy”: As agents become more capable, complexity increases, as do timelines; they are running for hours or days in some cases. Secondly, issues become catastrophic in sectors like healthcare or defense.

“This problem is getting a lot worse as models and agents improve,” Hylak said, “and I think there's good reason to believe it will continue to get worse.”

Raindrop AI's platform finds critical issues in agents in production and simulates fixes based on past user behavior, Hylak said. That lets teams confirm a fix works as intended before it's live, without introducing unexpected side effects.

The startup’s reinforcement learning (RL) platform optimizes harnesses and trains models directly from Raindrop data, he said. Its pre-deployment simulation engine helps identify what fixes would actually impact in production; its live A/B testing then shows those changes in action.

Messages, tool calls, retries, and errors are captured in one place, and human users are notified (typically via Slack) when there's an issue, he said. Models are trained for every customer, and signals are powering continual learning across models and harnesses. “It is condensed into something that is actually navigable, easy to understand, easy to verify,” Hylak said.

Arcade gives agents the security clearance they need to take action

AI agents are designed to do all kinds of things for you, but they often hit three major snags: authorization, governance, and reliability.

To act on behalf of real users with real permissions, agents need a new type of security architecture, said Sam Partee, co-founder and CTO of Arcade.dev.

Partee said his company’s secure agent runtime provides this authentication and authorization layer so agents can pass critical security reviews. It also provides observability so human users can watch everything an agent is doing. Actions are attributable to the exact moment in time with the least amount of privileged scopes.

Arcade is available in an installable plugin that can be deployed on-prem in a clean room-like environment; companies can continue to use their own sign-in and security tools, Partee said. Whenever anything is run in Arcade, it's gated by the same role-based access controls (RBACs), intrusion detection and prevention systems (IDPS), policies, entitlements, and other already-established checkpoints.

Arcade is tackling the supply chain attack problem, which has “gotten so rampant; it's unbelievable,” Partee noted. Security and observability have continued to be challenging because “largely, the abstraction has been wrong.”

Omilia is tackling the "not straightforward" CX problem

Solving enterprise customer experience (CX) is “really not straightforward,” said Claudio Rodrigues, CPO of Omilia.

Heuristic-based systems are controlled but slow; agentic systems are fast but unpredictable, Rodrigues said. Omilia built its platform to deliver both control and speed together.

The agentic, self-learning offering is built on a philosophy of observing customer service operations as they actually happen, rather than in the abstract. Omilia's agents observe problems first-hand, listen to every customer and agent interaction, ingest data, API specs, screen recordings, and standard operating procedures (SOP), then map those to use cases for customer support, he said.

Contact centers should be a revenue driver, Rodrigues said, and Omilia’s differentiator is its speech-to-text systems and governance and observability layers.

AI creates insights, suggests improvements, automatically generates conversational agents, pulls information from documents and APIs, and designs dialogue flows. Human experts can then test real and simulated interactions and deploy into production under their supervision. Omilia combines all of this into one enterprise-wide engine that continuously learns over time, Rodrigues said.

Rodrigues said the company handles more than 3 billion calls a year, 1 million-plus voice calls a day in some deployments, and has seen 30 to 45% improvement in time to resolution (TTR). Omilia’s agents generate 21x more upsell revenue versus human agents, he said.

In a mature deployment, automation “easily” reaches 80 to 90%, he said. However, “human in the loop is still very fundamental for us.”

Target SVP Siobhán Mc Feeney says the AI models her company runs aren't what gives Target its edge — everything built around them is.

"There's a lot in it. That to us is the moat," Mc Feeney said at VB Transform 2026. "The models are great, and they're important. They're just not sufficient to be the competitive advantage."

That discipline shows up early in how Target decides whether to build an agent at all. Mc Feeney was blunt, even "controversial" by her own admission, about the current AI moment: every enterprise wants AI agents, but not everything needs one, she said.

Agents earn their autonomy over time rather than getting it by default, she said — a principle that runs through everything Target has built around them.

Mc Feeney said the goal is to make sure agents are aimed at the problems that drive the most value for Target's guests. “We want to make sure we're investing in the right places," she said.

Being deliberate about agents

Agents are becoming part of Target's underlying architecture, increasingly connecting signals, systems, and decisions across supply chain, replenishment, and demand forecasting.

Mc Feeney framed it as retail's oldest promise — the right product, in the right place, at the right time — delivered at scale.

But her team has been deliberate about building AI agents, beginning with the simplest, most obvious question: What is the problem they’re trying to solve? This leads to several follow-on questions: 

• Does that problem need an agent? 

• If it does, what type of agent? An orchestrator? A super agent? A domain-specific agent? 

• Or is what you're calling an "agent" actually just a tool?

“You define that upfront, and this may sound a little process-heavy, then you have to register and certify your agent,” Mc Feeney said. Because a solution may already exist, and you don’t want to duplicate work. 

Agent design kicks off another series of important questions: What triggers an agent to act? Automation? An engineer? A timer? What needs to be put in place to track that? 

"We're trying to make sure we have lineage from the very beginning — the birthing of this agent, all the way through — because at 2 a.m. one morning, when something goes sideways, we want to make sure we understand everything that happened," Mc Feeney said.

Autonomy level is another consideration; new agents typically start with base autonomy and earn more over time. What the agent has access to is a separate question: what data, what systems, what tables, what databases?

Finally, there’s monitoring and observability; agents won’t solve problems, or improve over time, if they’re not continuously evaluated. 

“We measure everything: What it was intended to do, its calibration, its trajectory, not just runtime and latency,” Mc Feeney said. This creates full transparency, and allows agents to be tweaked over time. 

“You're talking about architecture and taxonomy and a data governance layer that absolutely had to be established,” she said.  

There's a lot in these "layers of autonomy" — that foundation is what gives Target the ability to scale and properly invest in the right models for the right problem.

Models have different “gradients” that are better for different jobs; for instance, frontier models excel at complex tasks that require crunching billions of pieces of data (like in heavy merchandising supply chains). But in some scenarios they can be cost-prohibitive. 

“So it’s making sure there's always a cost benefit,” Mc Feeney said. 

Agents must earn their autonomy

A digital-twin simulation predicted men's shorts inventory across three Target stores in Long Beach this summer — and one store came back needing six to seven times more stock than the others, she said. Inventory analysts' first reaction: That can't be right. But the system had found something they hadn't factored in. That store sat less than two miles from the beach; the other two were 10 to 12 miles inland. Analysts let the recommendation stand, and the stock sold through.

"This is science. This is mathematically more significant and more confidence-filling than humans doing it," Mc Feeney said. Results like that are what let Target's agentic systems earn more autonomy over time, she said.

Target looks at AI agent autonomy as "earned" and structures it as a four-level ladder, Mc Feeney said: agents start by making observations without acting, then move to suggesting actions while waiting for approval, then to acting within defined guardrails. At the highest level Target currently operates, agents run end-to-end — but still with a human in the loop.

“The autonomy levels for the agents are super important,” Mc Feeney said. “They earn them, and they can lose them if they don't perform as expected.” Models that drift will be taken out of service. 

As she put it, humans earn autonomy when we prove we can do something over time. Nobody is given a bunch of extra responsibilities just because; they have to have shown they’re able to handle them. 

In a similar way, agents can be scientifically measured and quantified: how accurate they were, how much they drifted, and how close they came to their intended goal. This helps establish guardrails, allowing builders to work faster, and “go fast forever,” because they're not constantly wondering where the guardrails are. 

“If you follow these guardrails, you [follow] security guidelines, you register the agent, and something still goes wrong, we have full lineage all the way through from the start,” Mc Feeney said. “Our ability to recover is much better.”

When it comes down to it, agent success is a confluence of factors, not just one, she said: “It's about your architecture. It's about your taxonomy. It's about the autonomy levels your agents have, and it's about security and observability.” 

A new skill set for new workflows

Even when agent autonomy is high, though, builders must still be held accountable when something goes wrong. Mc Feeney noted that teams are now working at speeds no one could have anticipated, which means evaluation harnesses have to be established and agents registered and tracked.

A lot of it is cultural; the workforce is being reshaped and builders and engineers need new skills to manage human workers and AI systems side by side. These contexts are quite different, but the career evolution is “super exciting.”

“You're a builder. You're observing agents building, and you're also coaching humans observing agents building,” Mc Feeney said. “The level of nuance is pretty special.”