Why Agentic Workflows Hit a Wall in Agreements, and How to Scale Past It

There's a critical question most enterprises aren't asking about agentic AI. Much attention goes to model benchmarks, orchestration frameworks, token economics. All real concerns. Yet none of them determine the thing that matters most: whether the agent actually completes the work, or grinds to a halt waiting for human sign-off on everything, routine and risky alike.

The decisive question is trust. Until the agent understands the commitments already in place and can be trusted within them, every action routes back through a human.

Every enterprise runs on agreements. Customer relationships are about delivering on what was promised in the Master Services Agreement, the SLAs, the renewal terms, and the price. Supplier relationships are about meeting the delivery milestones, payment schedules, liability clauses, and data obligations set out in the purchase agreement. Employee relationships are about operating within the offer, the NDA, the compensation plan and the confidentiality boundaries that define it. Strip away the org chart and the tech stack, and what's left is a web of commitments that answer three questions: What did we promise? What are we owed? What can we act on?

Humans have always navigated this. That's the job. The shift now underway in the age of agents is not that the rules are changing, it's that a new actor is being asked to follow them. Agents must understand prevailing terms at the reasoning stage, deliver on commitments, and escalate the exceptions they can't resolve. Same rules. Same commitments. New actor.

This is where most agentic deployments quietly break.

The agreement wall

Picture an agent about to take an action on behalf of a client. Without agreement context, the agent takes the baseline. It applies the same action to every client because it has no way to know that this client negotiated a different SLA, a different discount, a different data-handling clause. It breaks obligations it didn't know existed and produces generic outcomes. When something goes wrong, it creates exposure for enterprises.

The instinctive fix is to route everything back through a human. And so the agent escalates not the rare exception, but nearly everything, because it can't tell what's routine and what's risky. That's the wall. The ROI case for agentic AI was built on autonomy, and the moment every decision routes back through a person, the economics collapse. Even identical-terms renewals and standard NDAs end up in the same review queue as the genuinely risky exceptions.

The wall isn't a flaw in any one platform. It's structural. Any sufficiently capable agentic system will reach the point where the next action requires knowing the terms, and if it can't, it stops. Today, that means humans stay in the loop. The path forward is to systematically earn down that dependency, automating the genuinely routine as the agreement layer makes it safe to, and reserving humans for real exposure until autonomy is the rule and escalation the exception.

Agreements as a control plane

The way through is to treat agreements not as documents to be retrieved, but as a control plane for agentic architectures. Within that layer, there are two moments that matter.

The first is context at the decision. Before the agent acts, its obligations should be checked at runtime. Client-specific terms shape the path it takes. Policy gates decide when a human genuinely needs to weigh in and, just as importantly, when they don't. Every decision leaves an audit trail. This is the difference between an agent that guesses and an agent that decides correctly, and getting there is real work: a governance framework, deliberate context engineering, and a runtime policy engine that spans lines of business.

The second moment is binding the outcome. A decision becomes an agreement only when the right recipient signs. Without that step, an agent acts with no verification, no signature, no commitment of record, which means a dispute later and uncapped exposure. With identity verification and signing orchestration in place, the recipient is verified, the signature is captured and timestamped, and you have an audit-ready agreement of record suitable for use as evidence if there’s ever a dispute. The binding data then flows downstream so other enterprise agents can act on it instantly.

Decide correctly, then bind the commitment. Skip either half and the workflow stalls in blanket human review, which is exactly where the ROI stalls.

Closing the last mile takes its own foundation

It's tempting to assume a large model with good retrieval can handle all of this. It can't, because the last mile of agentic execution has requirements that are different in kind from generating text. 

It needs structured data: agreements as queryable records an agent can act on, not something that has to be reasoned over every time. It needs obligation activation, so the relevant agreement logic surfaces exactly where the agent operates. It needs controlled execution: human-in-the-loop based on risk exposure, and a clear line for when agents can operate on their own. And it needs deterministic enforcement, so agents operate with precision at scale rather than probabilistically.

Docusign Intelligent Agreement Management (IAM), powered by our AI engine Iris, is built to be that foundation. In practice, that foundation shows up as agreement-aware agents that can own specific workflows end-to-end — within the boundaries of what’s already been negotiated — and know when to hand control back to a person. Instead of treating every action as a one-off prompt, IAM gives those agents structured agreement data, obligation logic, and governed execution paths they can trust.

An open platform, not a walled garden

It works much better with an open platform. The agreement layer has to meet agents wherever they already run, which is why IAM is designed as an open platform with three entry points:

1. Model Context Protocol: Agreement context gets delivered where work actually happens: across AI surfaces like Copilot, ChatGPT, Claude and Gemini, and inside enterprise systems like Salesforce and ServiceNow. A copilot can answer an obligation question using live IAM context, whether that’s a Claude workspace analyzing a renewal or a Slackbot responding to “What termination rights do we owe Acme?” from a sales channel. You meet the agent where it runs.

2. Agent Studio: purpose-built agents on the IAM platform – both Docusign-built and partner-built – for jobs like NDA triage, price adjustment and renewal orchestration, and other repetitive agreement work. It packages domain expertise into reusable, governed agents that customers can adapt to their own playbooks and systems. Today, customers are already putting that foundation to work. A finance agent reconciles invoices against contracted pricing and payment schedules, auto-approving matches and escalating only true exceptions. A contract-renewal agent looks up negotiated price adjustments and service levels, generates a like-for-like renewal package, and routes it for signature while flagging any non-standard changes for the account team.

3. Agentic Workflows: Agents in other enterprise systems can call IAM agents, so coordination carries across the ecosystem rather than stopping at each system's edge. When a procurement agent in a buyer's ERP flags an expiring indirect-spend renewal, it calls an IAM agent that reads the governing agreements, checks the buyer's vendor and spend policy, and returns only the alternatives that clear it. The hard part isn't the lookup. It's knowing which options are actually in policy, and that lives in the agreement layer, not the calling system. IAM hands the cleared options back, and the decision is made where it belongs, by a person or an agent, depending on how that system is configured. IAM is the layer the coordination runs through, not another agent competing to make the call

Pick one. Pick all three. The agreement layer is available at every entry point, which means it strengthens the architecture you already have rather than asking you to rip and replace it.

The takeaway for enterprise leaders

If you're investing in agentic AI, the agreement wall is in your future whether you've named it yet or not. The leaders who get ahead of it will stop treating agreements as static records to be reasoned at run-time and start treating them as the control plane that governs what their agents are allowed to do. That's the move that turns an impressive demo into a workflow you can trust to run while the lights are off but agents are working 24x7: agreeing once, and operating anywhere.

The model race will keep making headlines. But the enterprises that win the agentic era will be the ones that solved the agreement problem first.

To learn how Docusign IAM and Iris can help your teams design, govern, and scale agreement-aware agents, connect with our experts and start mapping the workflows you want agents to own end-to-end.