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Bio, Work & Ideas

Peter Werry

Conference affiliation: Unblocked · 2026

Peter Werry is a founding engineer at Unblocked, where he builds context engines for coding agents that connect software with the decisions, conversations and expertise behind it. His goal is to help agents produce changes that reflect how a particular engineering organization actually works.

Based in Vancouver, Werry studied at the University of British Columbia and worked at Buddybuild and Apple before joining Unblocked. By September 2024, he was developing tools that combined source code with Slack conversations, documentation and issue trackers. His work now extends that organizational knowledge into autonomous coding, incident response and pull-request review.

  • Searchable information is not organizational understanding. Connecting tools, adding vector search or expanding context windows cannot tell an agent which decision matters, which sources disagree or why a previous approach failed. Werry favors targeted, personalized retrieval that connects code with review history, incidents and team conventions.
  • Satisfaction of search can derail coding agents. Borrowing a concept from radiology, he describes agents that stop after finding a plausible answer while overlooking an incident report, rejected implementation or relevant Slack conversation. Effective retrieval must continue across organizational boundaries without exhausting context or tokens.
  • A social engineering graph can locate actual expertise. Contribution histories, pull-request reviews and collaboration patterns help identify who understands particular systems and which decisions shaped them. Frequent activity alone does not establish authority. Unblocked’s document-query-engine workshop project illustrates structured retrieval over GitHub issues and pull requests.
  • Permission-aware organizational memory must remain current. Knowledge assembled from private channels and repositories should inherit their access restrictions. Werry also cautions against caching generated answers as durable truth: evolving code and documentation can make old outputs misleading, while unresolved conflicts should remain visible.

His AI Engineer Europe session connects these principles to ticket enrichment, context-aware reviews and background agents: software should reflect the accumulated judgment of the people responsible for maintaining it.

Read the topics behind these talks

2 conference talks

Key ideas

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A context engine connects code, decisions, permissions and expertise so agents can make changes that fit an organization without repeatedly rediscovering its history.

  • An agent starts where a new employee starts
    1:11 ↗
  • More autonomy makes context management harder
    4:31 ↗
  • A migration that erased a necessary dependency
    9:43 ↗
  • Why more search and more context are insufficient
    10:48 ↗
  • Turn connected evidence into usable context
    15:48 ↗
  • Put context into planning, review and support
    23:31 ↗
  • What the engine is—and how synthesis keeps permissions
    32:05 ↗
  • From human corrections to a collaboration graph
    39:45 ↗
  • Use the expert graph to decide where to look
    54:56 ↗
  • Expose context to autonomous work
    1:03:14 ↗
  • Contribution counts are only the first signal
    1:08:50 ↗
  • The workshop encounters a real access boundary
    1:13:00 ↗
  • Corrections become memory, then seed the next task
    1:20:42 ↗
  • Measure the whole task, including corrections
    1:27:45 ↗
  • Keep the graph fresh—and account for operating it
    1:34:41 ↗

Key ideas

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Peter Werry explains why coding agents need selected organizational knowledge—not merely repository access—and demonstrates how Unblocked connects code, decisions, review history, and expertise to improve planning and debugging.

  • Coding agents repeatedly rediscover repository structure, testing practices, deployment conventions, and organizational history because each task begins without the accumulated knowledge of an experienced teammate.
    0:58 ↗
  • Search access alone can produce “satisfaction of search”: the agent finds one plausible result and stops before discovering evidence that would change its plan.
    4:11 ↗
  • Useful context is selected for the task and connects code with intent, conventions, past decisions, discussions, and architecture rationale. Dumping everything into a large context window can exceed capacity and distract the agent.
    5:41 ↗
  • Sources make synthesized answers inspectable by humans and give coding agents direct references for deeper investigation.
    7:59 ↗
  • The planning demo reports about one minute and a sub-dollar cost with Unblocked, versus about two minutes and higher cost without it. The more consequential proposed benefit is avoiding downstream loops caused by incomplete discovery and wrong assumptions.
    11:25 ↗
  • Pull-request history can encode team-specific review practices, while expertise and review relationships can prioritize guidance and expose parts of the codebase with thin expert coverage.
    12:55 ↗

References