2027: The agent sprawl paradox
A team of 8 now has 240 agents running. None of them talk to each other. Why deploying more agents can make you less productive — and what to build first.
Endgame compiles every call, email, meeting, and playbook your team produces into one living context graph that people and agents can act on.
nvidia.com · Santa Clara
Top-5 customer; the March renewal and a Q4 expansion move as one $3.0M decision.
Champion Marcus Lee left in August. Priya Shah stepped in and opened a path to CFO Elena Ruiz — still the only thread, so the sponsor ask is on Thursday's agenda.
Security review 19 of 24, due Thursday. Rollout at 71% weekly active, +240 seats scoped for Q4.
✓ Overview + workstream agents · 12 sources · updated 12m ago
Leading GTM teams trust Endgame
When you compile context ahead of time, agents get a lot more performant.
interactions with agents
Every answer shows its sources.
Each fact links back to the file it came from, so anyone can verify it in one click.
Answers stick to your data.
Checked and organised before anyone asks. No guessing, no invented facts.
Instant answers.
Answering is a lookup: 140 to 330 times faster than reading the raw files.
A fraction of the cost.
About 40 times less text processed per answer. Reliable answers, far cheaper.
Measured from real product usage in September 2026, and carried forward since at that month's rate.
Claude, ChatGPT, Slack, your CLI, the apps you build: same answer, same citations, from the same context.
Your team already works here — so the context should too. Every answer comes back grounded and verified.
One place to see, correct, and govern everything your agents know — cited to source, grounded in your playbooks, and scoped to your permissions.
Every session is traced end to end: which agents ran, what they did, what they read, and what they touched.
Agents fix what they can and queue the rest for review. You approve or decline every change, so the graph never drifts on its own.
Spot something wrong? Push a correction in one line and it propagates through every page, answer, and agent at once.
Endgame learns your best practices, your definitions, and your semantics and applies them in every answer.
Champion went quiet after the Mar 3 pricing call[1] and the security review is the last open item.[2]
Every fact links back to the exact call, email, or record it came from, so anyone can verify any answer in seconds — nothing on faith.
Answers respect the access you've already structured — a rep sees their own book, a manager sees every account their team owns, nothing more.
We never train on your data. Enterprise-grade security, compliance, and control.
Single sign-on through your existing identity provider.
Encrypted in transit and at rest.
A reviewable record of every action an agent takes.
Salesforce, Gong, Slack, Teams, email, and more.
Any MCP client connects — Claude, ChatGPT, or your own.
Team and per-user activity, visible to your admins.
Customers see it where it counts — win rate, pipeline generated, and rep productivity.
53 of them, all reading one governed graph for a fraction of the tokens. That graph is Endgame.
Read the full case study →Ryan Vanshur built an always-on intelligence layer so Handle can query any strategic account, surface risks and stakeholder gaps, and align leadership on next steps.
Read the full case study →BetterUp's teams started with prototypes, but scaled into production with Endgame, unlocking myriad use cases across many teams.
Read the full case study →Hex didn't need more information. They needed the context from their existing systems connected and actionable when it mattered most.
Read the full case study →Read what our team has to say on applied AI research and what we're learning.
A team of 8 now has 240 agents running. None of them talk to each other. Why deploying more agents can make you less productive — and what to build first.
Behavioral data from real go-to-market professionals, not surveys or self-reports
A few years ago when a CRO wanted to understand why deals were stalling, it meant asking an analyst to sift through Salesforce, marketing data, call recordings, and email threads to assemble a narrative that felt complete.
Most Endgame demos end with the same question: This is cool. But couldn't we just build this ourselves in GPT or Claude?
Two ways in: build on the context layer yourself, or have our FDEs build it for you.
Talk to us