Agents could only post to named public channels - the messaging tool even
excluded direct spaces - so assignments, reports, and questions all crowded
into general. Now ChatService.ensureDirectSpace finds or creates the private
two-member conversation for any agent/employee pair; send_message takes
to="Name" for 1:1 traffic (exactly one of to/channel), read_messages reads a
conversation by teammate name, and task assignments land in the manager's DM
with the assignee. In a direct conversation the counterpart agent wakes on
every message without needing an @mention - same social contract as any
messenger - still bounded by the autonomous-turn budget, and wake task
briefings tell agents to reply with to= in kind. Covered by six new
direct-message contract tests; the assignment-thread test now encodes the
DM routing contract.
Agents now have a read_messages tool to check their inbox and channel
history during runs, enabling them to catch up on conversation before
acting. Task prompts are rewritten to encourage checking messages first
and discussing work with teammates rather than posting status updates.
MentionTextarea inserted raw agent names (e.g. @Sarah Chen) but the
backend regex captured only the first word (@Sarah), causing mention
resolution to fail silently. Moved canonicalMentionHandle to shared/
so the renderer can produce space-free handles that the backend resolves
correctly.
send_message answered the agent with "Posted to #x (message_id=...,
thread_id=...)" — a transaction receipt that gives no sense anyone is
there. An agent posting into that void has no reason to converse.
Now it answers with presence: who was addressed and can reply, who else
is in the channel, and the thread_id to stay in the conversation. The
message_id noise is gone. The tool's own description now frames it as
talking to teammates rather than posting a coordination status.
The GPT-account (Codex) path supports tool calls and worked before; my
forced tool_choice broke it and was the wrong fix. The real regression was
strict:null in the rebuild's tool definitions, which made the endpoint drop
the tool list so the model had nothing to call — a "Done" run that hired no
one. Forcing then turned that silent failure into a red one.
Restore the original "auto" loop (no forced tool_choice, no finish tool) and
keep only the one-line strict:null omission. Codex now receives valid tools
on the working "auto" choice, as in the version that worked.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Forcing only the opening turn let a weak model take one token action and
stop, so a kickoff hired one agent (or none) and went quiet. Now every turn
is forced (tool_choice required) and the agent ends only by calling the new
`finish` tool — so it must keep hiring, delegating, and messaging until the
work is actually done. Finishing with zero actions, or returning no tool
call at all, fails the run loudly instead of faking success.
Prompts tell each agent it must call a tool every turn and finish only when
done. Tests drive weak-model stubs that act then finish, proving a full team
forms rather than a single gesture.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The CEO's first run hired five reports into departments "general" and
"engineering"; the hiring-policy lookup was exact-match against the
seeded names (Executive, Engineering, ...), so every hire failed and
the kickoff produced nothing. The tool schema now enumerates the
canonical departments, the policy lookup matches case-insensitively
and stores the canonical name on the agent, and an unknown department
error lists the valid choices so the model can self-correct.
Agents can now create versioned company documents (PRD, spec, memo,
report, runbook, proposal, policy, guide) via the create_document tool.
Documents are persisted in the documents table and visible in the
Documents page.
The new app reached a real LLM but never ran an agent: the kickoff task_run
sat queued forever and runTask was a single text completion with no tools.
This adds the missing runtime:
- provider-tool-turn: OpenAI Responses + Anthropic Messages tool-use turns
- agent-tools + deliverable-writer: hire_agent, create_task,
update_task_status, write_deliverable (workspace-scoped, traversal-safe)
- agent-run-loop: real tool-use loop, capped iterations
- task-queue-worker: claims queued runs in a transaction and executes them
task-execution now drives the loop; a background worker auto-runs the
bootstrap CEO run so it hires its team, delegates tasks, and writes the first
deliverable into the bound workspace. Autonomy is gated by AGENTCOMPANY_NEW_AUTORUN
(tests run with it off to stay hermetic).
ZacharyZhang-NY
